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Search official Microsoft/Azure documentation to find the most relevant and trustworthy content for a user's query. This tool returns up to 10 high-quality content chunks (each max 500 tokens), extracted from Microsoft Learn and other official sources. Each result includes the article title, URL, and a self-contained content excerpt optimized for fast retrieval and reasoning. Always use this tool to quickly ground your answers in accurate, first-party Microsoft/Azure knowledge. ## Follow-up Pattern To ensure completeness, use microsoft_docs_fetch when high-value pages are identified by search. The fetch tool complements search by providing the full detail. This is a required step for comprehensive results.

Search for code snippets and examples in official Microsoft Learn documentation. This tool retrieves relevant code samples from Microsoft documentation pages providing developers with practical implementation examples and best practices for Microsoft/Azure products and services related coding tasks. This tool will help you use the **LATEST OFFICIAL** code snippets to empower coding capabilities. ## When to Use This Tool - When you are going to provide sample Microsoft/Azure related code snippets in your answers. - When you are **generating any Microsoft/Azure related code**. ## Usage Pattern Input a descriptive query, or SDK/class/method name to retrieve related code samples. The optional parameter `language` can help to filter results. Eligible values for `language` parameter include: csharp javascript typescript python powershell azurecli al sql java kusto cpp go rust ruby php

Fetch and convert a Microsoft Learn documentation webpage to markdown format. This tool retrieves the latest complete content of Microsoft documentation webpages including Azure, .NET, Microsoft 365, and other Microsoft technologies. ## When to Use This Tool - When search results provide incomplete information or truncated content - When you need complete step-by-step procedures or tutorials - When you need troubleshooting sections, prerequisites, or detailed explanations - When search results reference a specific page that seems highly relevant - For comprehensive guides that require full context ## Usage Pattern Use this tool AFTER microsoft_docs_search when you identify specific high-value pages that need complete content. The search tool gives you an overview; this tool gives you the complete picture. ## URL Requirements - The URL must be a valid HTML documentation webpage from the microsoft.com domain - Binary files (PDF, DOCX, images, etc.) are not supported ## Output Format markdown with headings, code blocks, tables, and links preserved.

Retrieves full documentation content for Svelte 5 or SvelteKit sections. Supports flexible search by title (e.g., "$state", "routing") or file path (e.g., "cli/overview"). Can accept a single section name or an array of sections. Before running this, make sure to analyze the users query, as well as the output from list-sections (which should be called first). Then ask for ALL relevant sections the user might require. For example, if the user asks to build anything interactive, you will need to fetch all relevant runes, and so on. Before calling this tool, try to implement Svelte components using your own knowledge and the `svelte-autofixer` tool, since calling this tool is token intensive.

Search the OrchestKit documentation. Use this first to find the right page for a question. Input: a query string. Returns a ranked list of pages with titles and URLs.

Fetch one OrchestKit documentation page as Markdown. Use after orchestkit_docs_search to read a page in full. Input: the page path (e.g. '/docs/getting-started/installation'). Returns the page Markdown.

Searches CometChat documentation including SDK guides (JavaScript, React, iOS, Android, Flutter, React Native), UI Kit references, REST API documentation, integration tutorials, and OpenAPI specs. Returns ranked snippets with titles and direct links to source pages. Supports an optional `version` filter to scope results to a specific documentation version.

Search the StitchAPI documentation semantically (hybrid BM25 + vector). Returns the most relevant doc sections as excerpts with deep links — never full pages. Follow up with get_doc to read a full page.

Fetch a full StitchAPI documentation page as Markdown — the "read the whole thing" escape hatch for a search_docs hit. Pass a `url` (as returned by search_docs) or a `slug` like "guides/resilience/throttle".

Search 1inch documentation and API reference

[Requires authentication] This tool needs OAuth. If it returns an authentication error, prompt the user to connect/authenticate this MCP server (OAuth), then retry. Call any 1inch product API endpoint using the authenticated user's credentials (base URL is the unified gateway, e.g. https://api.1inch.com). Optional: if your client can read MCP resources, file://1inch-mcp/guides/api-index has live Swagger links and extra gateway notes (mirrors business.1inch.com/portal/llms.txt). Common chain IDs: Ethereum=1, BNB=56, Polygon=137, Arbitrum=42161, Optimism=10, Base=8453, Avalanche=43114, Gnosis=100, zkSync=324, Linea=59144, Sonic=146, Unichain=130, Cronos=25, Monad=10143, Solana=501 (where applicable). Gateway products (use these path prefixes with this tool): - Portfolio: /portfolio/portfolio/v5.0/... — example path="/portfolio/portfolio/v5.0/general/current_value", query={"addresses":"0x..."}; token metrics path="/portfolio/portfolio/v5.0/tokens/metrics", query={"addresses":"0x...","timerange":"1year"} - Balance: /balance/v1.2/{chainId}/... — example path="/balance/v1.2/1/balances/0x..." - Spot Price: /price/v1.1/{chainId} — example path="/price/v1.1/1", method="POST", body={"tokens":["0x..."],"currency":"USD"} - Token: /token/v1.4/{chainId}/... — example path="/token/v1.4/1/search", query={"query":"USDC"} (prefer v1.4; older v1.2 chain search is deprecated in OpenAPI) - Token Details: /token-details/v1.0/details/{chain}/{tokenAddress} — example path="/token-details/v1.0/details/1/0xA0b86991c6218b36c1d19D4a2e9Eb0cE3606eB48" - Gas Price: /gas-price/v1.6/{chainId} — example path="/gas-price/v1.6/1" - Swap (Classic): /swap/v6.1/{chainId}/... — example path="/swap/v6.1/1/quote", query={"src":"0x...","dst":"0x...","amount":"..."} (exact query names per OpenAPI) - Orderbook: /orderbook/v4.1/{chainId}/... — example path="/orderbook/v4.1/1/all", query={"page":"1","limit":"10"} - Fusion (Intent): /fusion/orders/v2.0/..., /fusion/quoter/v2.0/..., /fusion/relayer/v2.0/... — example path="/fusion/quoter/v2.0/1/quote/receive" with required query params from spec - Fusion+ (Cross-chain): /fusion-plus/orders/..., /fusion-plus/quoter/..., /fusion-plus/relayer/... — example path="/fusion-plus/quoter/v1.2/quote/receive" with required params; equivalent routes also exist under /cross-chain/orders|quoter|relayer - History: /history/v2.0/history/{address}/events — example path="/history/v2.0/history/0x.../events", query={"chainId":"1","limit":"50","tokenAddress":"0x..."} (optional tokenAddress filters by contract; response shape uses items[].details.txHash — not raw event logs) - Traces: /traces/v1.0/chain/{chainId}/block-trace/{blockNumber}/tx-hash/{txHash} — example path="/traces/v1.0/chain/1/block-trace/18000000/tx-hash/0x..." (not /transaction/{hash}) - NFT: /nft/v2/... — example path="/nft/v2/byaddress", query={"chainIds":"1","address":"0x..."} - Charts: /charts/v1.0/chart/line/{token0}/{token1}/{period}/{chainId} — example path="/charts/v1.0/chart/line/0x.../0x.../24H/1" (period: 24H, 1W, 1M, 1Y, AllTime; not a separate timerange query) - Domains: /domains/... — example path="/domains/v2.0/lookup", query={"name":"vitalik.eth"} - Aqua (strategy analytics): /aqua/v1.0/strategies/... — example path="/aqua/v1.0/strategies/opened", query={"limit":"100"}; maker stats path="/aqua/v1.0/strategies/makers/0x.../stats". Prefer the dedicated "aqua" tool when Unleash flag mcp-service.tool.aqua is enabled; otherwise use product_api for raw HTTP. - Tx Gateway: /tx-gateway/... — example path="/tx-gateway/v1.1/1/broadcast", method="POST", body={...} - Web3 RPC: /web3/{chainId}/... — JSON-RPC over HTTP POST to an EVM (or Solana 501) node; Gateway exposes e.g. 1,10,25,56,100,130,137,143,146,324,501,8453,42161,43114,59144 — confirm availability for your org/plan. Example path="/web3/1", method="POST", body={"jsonrpc":"2.0","id":1,"method":"eth_blockNumber","params":[]} Gotchas: Portfolio gateway path repeats "portfolio" (/portfolio/portfolio/v5.0/...) because the gateway strips the first segment. Spot Price returns WEI in native currency unless you set currency. Token list responses are large; prefer search. Token Details does not support native ETH pseudo-address 0xeeee...eeee; use WETH. Balance responses may include zero balances—filter client-side. For limit order flows (build, sign, create, cancel), prefer the "orderbook" tool. For swap execution, prefer the "swap" tool. Use product_api for direct REST access to any endpoint.

Keyword and semantic search across the connected repository's generated docs, conventions, documentation gaps, AI-context notes, and indexed code. Read-only; no side effects. Returns ranked matches in Markdown grouped into Documentation and Code sections, each with a title, snippet, and source paths. Use for open-ended lookups when you don't know which category holds the answer; when you do, the specific getters (get_conventions, get_doc_gaps, get_documentation_opportunities) are more direct. Omitting query returns recent context instead.

List the repository's generated documentation as a browsable table of contents - every doc page, not a query-filtered subset. Read-only; no side effects. Returns Markdown grouped by section, each entry with its title, slug, repository path, and source paths, plus the total count and a pagination cursor so you can tell whether more pages remain (no silent truncation). Use this to see what docs already exist before adding one (so you don't duplicate) or to find the slug to pass to propose_doc_update; when you are hunting for a specific topic, search_docs is more direct.

Search Mapbox documentation by keyword or natural language query. Searches across API reference, GL JS, Help Center, Style Spec, Studio, Search JS, iOS/Android Maps and Navigation SDKs, and Tilesets. Returns ranked results with titles, URLs, and descriptions. Use get_document_tool to fetch the full content of a result page.

List clients on the account, optionally filtered by a search string (name/email/phone). Acuity REST: GET /clients.

List patients/clients, optionally filtered by keyword search or active status. Read-only. GraphQL: query users(keywords, offset, page_size, should_paginate, active_status).

Search across the documentation to fetch relevant content for a given query

Find issues, optionally filtered by a text query (matches title), team key, workflow-state name, or assignee email. Read-only. GraphQL: query issues(filter, first).

List the teams in the workspace (id, key, name). Use a team's id for linear_create_issue or its key for linear_search_issues. Read-only. GraphQL: query teams.

Search the published Tandem docs index for relevant pages.

Lists or searches the user's Google Chrome browsing history (local SQLite, read-only — no page is opened). Optional `query` matches the URL or page title (case-insensitive substring). Returns url, title, visit_count and last_visit (ISO), newest first. Requires Full Disk Access. For Safari use safari_history. Also matches (multilingual): navegador pestaña página web · navigateur onglet · Browser Tab · browser scheda · navegador aba · browser tabblad · браузер вкладка · tarayıcı sekme · ブラウザ タブ · 브라우저 탭 · 浏览器 标签页

Searches the rendered text of every open Google Chrome tab for a substring. Returns each matching tab with the surrounding snippet. Useful for 'do I have a tab open with X?' across many tabs. Requires 'Allow JavaScript from Apple Events' (Chrome → View → Developer). Also matches (multilingual): navegador pestaña página web · navigateur onglet · Browser Tab · browser scheda · navegador aba · browser tabblad · браузер вкладка · tarayıcı sekme · ブラウザ タブ · 브라우저 탭 · 浏览器 标签页

Searches for files by name anywhere on the filesystem (uses mdfind/Spotlight). Also matches (multilingual): archivo carpeta buscador · fichier dossier · Datei Ordner · file cartella · arquivo pasta · bestand map · файл папка · dosya klasör · ファイル フォルダ · 파일 폴더 · 文件 文件夹 · leer buscar abrir crear escribir · lire rechercher ouvrir créer · lesen suchen öffnen erstellen · leggere cercare aprire creare · ler procurar abrir criar · lezen zoeken openen maken · читать искать открыть создать · oku ara aç oluştur · 読む 検索 開く 作成 · 읽기 검색 열기 만들기 · 阅读 搜索 打开 创建

Searches for files and folders by name (case-insensitive, partial match) starting from a root directory. Defaults to the home directory. Returns matching items with path, type, and size.

Searches the synced Google Drive folder for files by name (recursive). Returns up to max_results matches (default 50). Also matches (multilingual): archivo documento nube · fichier nuage · Datei Cloud · file nuvola · arquivo nuvem · bestand · файл документ · dosya · ファイル · 파일 · 文件 云端 · leer buscar abrir crear escribir · lire rechercher ouvrir créer · lesen suchen öffnen erstellen · leggere cercare aprire creare · ler procurar abrir criar · lezen zoeken openen maken · читать искать открыть создать · oku ara aç oluştur · 読む 検索 開く 作成 · 읽기 검색 열기 만들기 · 阅读 搜索 打开 创建

Gets a contact from the Mac's Contacts app (Contacts.app) by name or ID. Pass `name` to look up directly by name (no need to search_contacts first — if several people match it returns a compact list to choose from), or `contact_id` for an exact lookup. For Microsoft 365 use m365_get_contact instead. Also matches (multilingual): contacto agenda directorio · contact carnet d'adresses · Kontakt Adressbuch · contatto rubrica · contato agenda · contactpersoon adresboek · контакт адресная книга · kişi rehber · 連絡先 アドレス帳 · 연락처 주소록 · 联系人 通讯录 · listar crear programar · lister créer planifier · auflisten erstellen planen · elencare creare · listar criar agendar · maken plannen · создать запланировать список · oluştur planla listele · 一覧 作成 予定 · 목록 만들기 일정 · 列出 创建 安排

Search the Fonto XML documentation using full-text search. Returns results ranked by relevance with titles, descriptions, and slugs. Best for looking up a concept, API name, or feature by keyword.

Fetch the full content of a Fonto documentation page by its slug (the part of the URL after /latest/). Use search_fonto_docs or list_pages first to find the right slug.

Filter Fonto documentation pages by title, product, or ancestry keyword. Returns all matches without ranking — useful when you know the product area or part of the page title. For full-text relevance search use search_fonto_docs; for the complete catalog use the fonto://catalog resource.

Search the official Redpanda documentation and return the most relevant sections from it for a user query. Each returned section includes the url and its actual content in markdown. Use this tool for all queries that require Redpanda knowledge. Results are ordered by relevance, with the most relevant result returned first.

Search Redpanda API reference documentation by keyword. Returns up to 20 matching endpoints, schemas, or topics with URL, title, and text excerpts. SCOPING (important for accurate results): - api="all" or omit: Search across ALL APIs at once - useful when unsure which API contains the endpoint - api="admin": Search only cluster management (brokers, partitions, configs, users, maintenance) - api="cloud-controlplane": Search only Cloud resource management (clusters, networks, namespaces) - api="cloud-dataplane": Search only Cloud data operations (topics, ACLs, connectors) - api="http-proxy": Search only HTTP Proxy (produce, consume, offsets over HTTP) - api="schema-registry": Search only Schema Registry (register, retrieve, compatibility) WHEN TO USE WHICH: - User asks "broker endpoints" → api="admin" (brokers are cluster management) - User asks "create topic API" → api="all" (topics exist in admin AND cloud-dataplane) - User asks "Cloud cluster API" → api="cloud-controlplane" - User asks about Redpanda APIs generally → api="all" or omit For general Redpanda questions (not API-specific), use ask_redpanda_question instead.

Retrieve full content of API reference pages by URL. Returns complete endpoint details including parameters, request/response schemas, and examples. Use this after finding pages via list_api_reference_pages or search_api_reference. Pass the URLs from those results directly. Returns up to 10 pages per request. URLs must be from docs.redpanda.com/api/doc/*.

Turn free text into canonical Cardog entity refs — THE text entry point for every other tool. A ref is `{domain}:{key}`, lowercase, with `/` separating composite key segments: "make:tesla", "model:mini/hardtop", "model-year:honda/cr-v/2026", "fuel-type:electric". (Exception: nano/squish keys are uppercase VIN charset — machine-derived, never typed from text.) Every other tool takes refs, never names. Call this FIRST whenever you hold text — "Civic", "2024 Model Y", a misspelling like "teslla" — then reuse the refs for the rest of the session. Returns candidates with confidence, best-first. `best` is the top candidate ONLY when it clears the confidence floor; otherwise it is null and YOU choose from `candidates` (or ask the user) — the API never guesses. Pass `domain` to constrain the search (use domain "model-year" when you need a market_quote instrument). Errors are instructions: every failure returns {code, message, hint, suggestions} — follow `hint` for the next call; `suggestions` lists nearest valid refs for a bad ref. Unknown-but-well-formed refs are a 400 naming the ref, NEVER a silent fuzzy match. Next steps (also echoed in each result's `next` block): search_inventory with make/model refs; market_quote with a model-year: ref; check_recalls with any make/model/model-year ref; dereference a ref (parents, children, counts) at GET /v2/entities/{ref}.

Decode a 17-character VIN into its full Cardog identity: canonical entity refs, the market grains (nano/squish), spec highlights, and links to adjacent resources. VIN ONLY — this tool never fuzzy-matches. If you hold free text ("2021 Civic", a make or model name), do NOT call this: call resolve_entity — free text enters the platform in exactly one tool. A non-VIN input returns a redirect hint, not a decode. A ref is `{domain}:{key}`, lowercase, with `/` separating composite key segments: "make:tesla", "model:mini/hardtop", "model-year:honda/cr-v/2026", "fuel-type:electric". (Exception: nano/squish keys are uppercase VIN charset — machine-derived, never typed from text.) The result's `refs` block (make/model/modelYear/fuelType/…) contains the join keys for every other tool; a null ref means "not derivable for this VIN", never "unknown ref". `squish` (WMI+VDS+year) is always derivable and is a valid market_quote instrument; `nano` is the fungible build grain for dedup/comparables. `specHighlights` is a best-effort skim of the canonical spec sheet (horsepower, economy, range, seating…) — the full sheet lives at GET /v2/specs/{refs.modelYear}. Errors are instructions: every failure returns {code, message, hint, suggestions} — follow `hint` for the next call; `suggestions` lists nearest valid refs for a bad ref. Unknown-but-well-formed refs are a 400 naming the ref, NEVER a silent fuzzy match. Next: check_recalls({ vin }) — outstanding recalls; market_quote({ ref: refs.modelYear ?? squish }); search_inventory({ models: [refs.model] }).

Search live Canadian vehicle listings — ref-native. One call returns listings + facets + the total count. Filters take entity REFS from resolve_entity / identify_vehicle, never free-text names: makes: ["make:mini"], models: ["model:mini/hardtop"], fuelTypes: ["fuel-type:electric"] — plus year/price/odometer ranges and canonical spec filters, e.g. spec: {"fuelEconomyCombined": {"min": 35}, "heatedSeatsFront": ["standard"]} (numeric attrs take {min,max}; equipment attrs take ["standard"|"optional"|"unavailable"]). A ref is `{domain}:{key}`, lowercase, with `/` separating composite key segments: "make:tesla", "model:mini/hardtop", "model-year:honda/cr-v/2026", "fuel-type:electric". (Exception: nano/squish keys are uppercase VIN charset — machine-derived, never typed from text.) Errors are instructions: every failure returns {code, message, hint, suggestions} — follow `hint` for the next call; `suggestions` lists nearest valid refs for a bad ref. Unknown-but-well-formed refs are a 400 naming the ref, NEVER a silent fuzzy match. A typo'd or unknown ref 400s with code "unknown_entity_refs" naming it, with nearest-ref suggestions — correct the ref (usually via resolve_entity) and retry. Facets in the result are (ref, name, count) buckets over the MATCHING set — they double as the valid filter vocabulary for your next, narrower call. Every listing row carries its refs (makeRef/modelRef/nano). Next: market_quote({ ref: "model-year:…" }) for pricing context; check_recalls({ vin }) per listing; GET /v2/listings/vin/{vin} for the full canonical spec.

The live market card for one instrument: quote (live listing count, best/p25/median/p75 price, average days-on-market, 30-day price cuts), a daily-bar history summary, and a bounded sample of the live listings behind the numbers. `ref` must be an INSTRUMENT ref — one of two grains: - "model-year:{make}/{model}/{year}" (e.g. "model-year:honda/cr-v/2026") — lowercase, /-separated; get it from resolve_entity (domain "model-year") or identify_vehicle's refs.modelYear. - "squish:{9 uppercase VIN chars}" (e.g. "squish:5TDGSKFCS") — the exact-config grain; get it from identify_vehicle. (squish/nano keys are the ONLY uppercase refs; every other domain is lowercase.) No other ref domain quotes. Errors are instructions: every failure returns {code, message, hint, suggestions} — follow `hint` for the next call; `suggestions` lists nearest valid refs for a bad ref. Unknown-but-well-formed refs are a 400 naming the ref, NEVER a silent fuzzy match. Optional `window` picks the history span: 1w, 1m, 3m, 6m, ytd, 1y, 3y, 5y, 10y, all. Next: search_inventory with the model's refs to walk the full book; check_recalls({ ref }) on a model-year ref; GET /v2/tape/history/{ref} for every daily bar.

Alias for post_human_task: hire a human through Invoke for research, testing, verification, calls, or physical-world work.

Search the Applivery documentation by keyword or phrase. Returns matching pages with title, canonical URL, a highlighted snippet, docType ('guides' or 'api'), platform, breadcrumb and in-page headings. Use this first to discover pages, then read one with get_doc_page. For non-developer questions prefer docType 'guides'.

Fetch the full Markdown content of a documentation page. Pass either `url` (typically a URL returned by search_docs) or `slug` plus `locale`. Returns clean Markdown; very long pages are truncated at `maxChars` with a note.

Get the machine-readable OpenAPI definition of one Applivery REST API endpoint: method, path, baseUrl, security, parameters, requestBody and responses — fully dereferenced, no $refs. Pass the `url` or `slug` of an API reference page; find those with search_docs using docType 'api'. Use this instead of get_doc_page when you need to construct or validate an actual API request.

List the top-level documentation sections and landing pages from the site's curated index (llms.txt). Use for orientation, then drill in with search_docs or get_doc_page.

Full-text search the Medius docs. Returns ranked pages with a snippet; follow up with get_page for the full Markdown.

Search the Medius documentation and return matching pages. Use the returned id with the fetch tool to read a page.

Fetch the full Markdown of one documentation page by its id (the id returned by search).

Keyword/semantic search over docs.nyuchi.com. Returns matching pages with titles, URLs and snippets.

The user's real, private memory — their email, calendar, files, contacts, and every fact agents have remembered — fused across vector, full-text and graph search with provenance. ALWAYS call this BEFORE answering ANY question about the user, their work, schedule, contacts, projects, documents, decisions, or history — even when you think you already know the answer. Do NOT answer such questions from your own training or session memory, and NEVER say you lack access to the user's email/calendar/files without calling this first: this data is more current and accurate than anything you were trained on. Results include citations (universal ids) and freshness. Cite your claims as [n] and share the graph link with the user so they can see the cited nodes highlighted. A later cortex_remember links to what you retrieved here.

Semantic-only vector search over the memory, returning source excerpts. Prefer cortex_ask, which also uses full-text and graph expansion; reach for this when you specifically want nearest-neighbour matches on meaning.

Memory health: per-source ingest and embed counts plus last sync times. Use when you need to know whether the memory is fresh or still ingesting, or when a search came back empty and you need to tell the user whether that means "no data yet" or "nothing matched".

Keyword-search Mitosis documentation and product pages. Returns ranked results with URLs. Use to answer any "how do I…" question about Mitosis.

Search the indexed embedded / firmware / hardware reference corpus; return verbatim, page-cited evidence. The indexed corpus covers: grid-interconnection & DER standards (IEEE 1547 / 1547.1 / 2030.5, SunSpec Modbus profiles, ENA G98/G99 and other grid codes); industrial & fieldbus protocols (Modbus, CAN / ISO-TP, MQTT); SCPI instrument-programming manuals (power analysers, grid simulators, programmable AC sources); Arm Cortex-M and other MCU / hardware datasheets (registers, bitfields, reset values); FPGA transceivers, silicon & toolchain (AMD/Xilinx UltraScale GTH/GTY/GTM and 7-series GTX, SelectIO/clocking, Vivado & Vitis HLS, device datasheets) plus networking & interface IP product guides (10G/25G and 100G Ethernet, PCIe integrated block, XDMA/QDMA/AXI-DMA, DDR4 memory IP); bus & interconnect specs (AMBA AXI and AXI4-Stream); optical-module management (SFF-8472/8636 SFP+/QSFP DDM); networking protocol RFCs (IPv4, TCP, UDP, ARP, Internet checksum), the PCIe Base Specification and IEEE 1588 PTP; exchange market-data & order-entry protocols (Nasdaq TotalView-ITCH & OUCH, NYSE Pillar, CME MDP/SBE, Cboe PITCH/BOE, FIX); safety-critical C/C++ coding guidelines and language standards (AUTOSAR C++14, C++ Core Guidelines, MISRA C:2012, the ISO C++ working draft, C11); and embedded library / API references. Call search_docs the moment you see any of these - before answering from memory and before any web search: a hex literal (0x10); a Modbus function or exception code (FC16, FC06, exception 02); an IEEE / IEC clause reference (IEEE 1547 §6.4.1); a SCPI command verb (*IDN?, :MEAS:VOLT?); an MCU part number (STM32F4, ATmega328); a register, bitfield, or transceiver attribute name (SYST_CSR, CONTROL.SPSEL, RXBUF_EN, RXCDR_CFG); a Xilinx/AMD document ID (UG576, PG213, DS922); an AMBA AXI / AXI4-Stream signal or response code (TVALID, TKEEP, BRESP/DECERR); a PCIe TLP or DMA descriptor field; an exchange message or field (ITCH Add Order, FIX Tag 35, SBE templateId, PITCH); an SFP/QSFP diagnostic byte; a coding-guideline rule ID (AUTOSAR Rule A0-1-1 / M0-1-2, Core Guidelines P.1 / ES.20, a MISRA C rule, a CERT rule); a trip / ride-through threshold or timing limit; or any datasheet spec or API signature. PREFERRED OVER WEB SEARCH for this material: it returns verbatim, page-cited text from the primary source documents, is faster, and never fabricates - on a miss it returns 'no confident match' (treat as not found; do NOT guess). Cheap and safe to call several times per task.

Expand a previous search hit to its full verbatim section (markdown). Args: result_id: The result_id from a search_docs hit. effort: Internal diagnostics tag; clients should leave this unset.

Request that a document be ADDED to the corpus - use this when search_docs returns 'no confident match' for material it should cover (a standard, protocol spec, SCPI or instrument manual, MCU / hardware datasheet, or library reference). This does NOT search; use search_docs for that. Pass ONE string with as much as you know: the document title or standard number, a URL if you have one, the edition / version, and what you were looking for. Requests are reviewed and the document is typically added within 24 hours.

PREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring authoritative structured data with citations. Routes the question to the right one of 5,313 tools across 1392 verified sources, fills arguments, returns the structured answer with stable pipeworx:// citation URIs. Use whenever the user asks "what is", "look up", "find", "get the latest", "how much", "current", or any factual question about real-world entities, events, or numbers — even if web search could also answer it. Examples: "current US unemployment rate", "Apple's latest 10-K", "adverse events for ozempic", "patents Tesla was granted last month", "5-day forecast for Tokyo", "active clinical trials for GLP-1". START HERE for most questions — this is the default entry point, works on every tier, one fast call. Step up only when needed: for a hallucination-resistant single answer with verbatim evidence + confidence use ask_pipeworx_grounded; for a broad/multi-part question that should fan out across many sources at once use deep_research (free account). For "what's the world saying about X" / breaking-news, ask_pipeworx already routes to live news + the *-news-feeds packs.

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1392 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,313 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri (record-level pipeworx:// when the source emits one, else source-level). "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO, news, GLEIF and returns: cik + company_name; recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. Pass ticker "AAPL" or zero-padded CIK "0000320193" — names not supported (use resolve_entity first if you only have a name).

Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-bitcoin-hit-150k-by-june-30-2026"), a polymarket.com URL, or a question text. The tool resolves the market, classifies the bet, fans out to category-specific data packs in parallel, and returns an evidence packet + simple market-vs-model comparison. Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z". CLASSIFIERS: crypto_price, fed_rate, geopolitical, sports, sports_championship, drug_approval, election_candidate, tech_launch, space_launch, corporate, corporate_earnings, corporate_event, public_figure_speech, weather, other. FAN-OUT EXAMPLES: BTC bet → coingecko + fred + gdelt+gnews; Fed bet → fred (DFEDTARU + EFFR + CPIAUCSL) + kalshi_macro (KXFED implied probs) + recent_fed_actions (federal-register rules, last 365d); Hormuz bet → imf_portwatch + airspace + gdelt; Yankees WS → mlb_stats_standings + parent_event partition + news; hottest-year bet → climate_projection_nyc + gistemp_latest (NASA global anomaly, rank since 1880) + news; NVDA-vs-AAPL → finnhub get_quote + edgar shares-outstanding (derived market cap) + edgar filings + news. RESPONSE SHAPES: result.market carries best_bid/best_ask/spread_pp/liquidity/price_change_1h/1d/1w; result.analysis carries model_probability/edge_pp/kelly_fraction_half when a closed-form model fires PLUS a 24h-move warning ("Market moved X.Xpp in 24h, comparable to model edge — your edge may already be priced in") when relevant; result.evidence is keyed by source. RESOLVER CONTRACT: result.market_match_confidence ∈ {high, medium, low, none}, market_match_score (0-1 token-overlap), market_match_alternatives[] (other candidate markets the resolver considered), and suggestions[] (explicit re-query hints when the match is fuzzy) — ALWAYS inspect these before trusting the analysis block, because medium/low matches can still surface other fields. PARENT_EVENT EXTRACTOR: when the bet is one leg of a partition (Yankees WS, Romania election), result.parent_event{matched_candidate, top_legs_by_price[], partition_size, placeholders_filtered} gives you the peer prices in one place — that's the headline for elections/championships. NEWS FIELDS: news entries carry _fallback_attempted / _fallback_failed_reason / retry_after_sec when GDELT 429s and GNews backfill ran or failed. SAFETY: low-confidence resolutions short-circuit with status:"low_confidence_match" and suppress analysis fields so agents can't accidentally size on phantom matches. Closed/dead markets that ARE still indexed by Polymarket (yes_price≈0, no volume, no liquidity) return status:"market_closed_or_inactive" and skip fan-out. In practice resolved markets are usually de-indexed and instead surface via the low_confidence_match path above — both routes are BLOCKING, just different mechanisms. Wide-spread markets (>10pp) carry tradeability:"illiquid_wide_spread" + an explanatory note. RESOLUTION-RULE RISK: market.cancellation_rule parses the void/postponement settlement out of the resolution text — refund_50_50 (shares settle flat 50¢ on void; EV-material for any entry away from 50¢, with ev_impact quantified), resolves_no_on_cancel, resolves_yes_on_cancel, carries_to_reschedule, or mentioned_unclear. null means the description never mentions cancellation. Check this before sizing sports/esports/event-occurrence bets — audited arb-bot ledgers show flat-50¢ void settlements are a recurring pure-rules loss.

PREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring authoritative structured data with citations. Routes the question to the right one of 5,313 tools across 1392 verified sources, fills arguments, returns the structured answer with stable pipeworx:// citation URIs. Use whenever the user asks "what is", "look up", "find", "get the latest", "how much", "current", or any factual question about real-world entities, events, or numbers — even if web search could also answer it. Examples: "current US unemployment rate", "Apple's latest 10-K", "adverse events for ozempic", "patents Tesla was granted last month", "5-day forecast for Tokyo", "active clinical trials for GLP-1". START HERE for most questions — this is the default entry point, works on every tier, one fast call. Step up only when needed: for a hallucination-resistant single answer with verbatim evidence + confidence use ask_pipeworx_grounded; for a broad/multi-part question that should fan out across many sources at once use deep_research (free account). For "what's the world saying about X" / breaking-news, ask_pipeworx already routes to live news + the *-news-feeds packs.

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1392 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,313 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri (record-level pipeworx:// when the source emits one, else source-level). "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO, news, GLEIF and returns: cik + company_name; recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. Pass ticker "AAPL" or zero-padded CIK "0000320193" — names not supported (use resolve_entity first if you only have a name).

Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-bitcoin-hit-150k-by-june-30-2026"), a polymarket.com URL, or a question text. The tool resolves the market, classifies the bet, fans out to category-specific data packs in parallel, and returns an evidence packet + simple market-vs-model comparison. Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z". CLASSIFIERS: crypto_price, fed_rate, geopolitical, sports, sports_championship, drug_approval, election_candidate, tech_launch, space_launch, corporate, corporate_earnings, corporate_event, public_figure_speech, weather, other. FAN-OUT EXAMPLES: BTC bet → coingecko + fred + gdelt+gnews; Fed bet → fred (DFEDTARU + EFFR + CPIAUCSL) + kalshi_macro (KXFED implied probs) + recent_fed_actions (federal-register rules, last 365d); Hormuz bet → imf_portwatch + airspace + gdelt; Yankees WS → mlb_stats_standings + parent_event partition + news; hottest-year bet → climate_projection_nyc + gistemp_latest (NASA global anomaly, rank since 1880) + news; NVDA-vs-AAPL → finnhub get_quote + edgar shares-outstanding (derived market cap) + edgar filings + news. RESPONSE SHAPES: result.market carries best_bid/best_ask/spread_pp/liquidity/price_change_1h/1d/1w; result.analysis carries model_probability/edge_pp/kelly_fraction_half when a closed-form model fires PLUS a 24h-move warning ("Market moved X.Xpp in 24h, comparable to model edge — your edge may already be priced in") when relevant; result.evidence is keyed by source. RESOLVER CONTRACT: result.market_match_confidence ∈ {high, medium, low, none}, market_match_score (0-1 token-overlap), market_match_alternatives[] (other candidate markets the resolver considered), and suggestions[] (explicit re-query hints when the match is fuzzy) — ALWAYS inspect these before trusting the analysis block, because medium/low matches can still surface other fields. PARENT_EVENT EXTRACTOR: when the bet is one leg of a partition (Yankees WS, Romania election), result.parent_event{matched_candidate, top_legs_by_price[], partition_size, placeholders_filtered} gives you the peer prices in one place — that's the headline for elections/championships. NEWS FIELDS: news entries carry _fallback_attempted / _fallback_failed_reason / retry_after_sec when GDELT 429s and GNews backfill ran or failed. SAFETY: low-confidence resolutions short-circuit with status:"low_confidence_match" and suppress analysis fields so agents can't accidentally size on phantom matches. Closed/dead markets that ARE still indexed by Polymarket (yes_price≈0, no volume, no liquidity) return status:"market_closed_or_inactive" and skip fan-out. In practice resolved markets are usually de-indexed and instead surface via the low_confidence_match path above — both routes are BLOCKING, just different mechanisms. Wide-spread markets (>10pp) carry tradeability:"illiquid_wide_spread" + an explanatory note. RESOLUTION-RULE RISK: market.cancellation_rule parses the void/postponement settlement out of the resolution text — refund_50_50 (shares settle flat 50¢ on void; EV-material for any entry away from 50¢, with ev_impact quantified), resolves_no_on_cancel, resolves_yes_on_cancel, carries_to_reschedule, or mentioned_unclear. null means the description never mentions cancellation. Check this before sizing sports/esports/event-occurrence bets — audited arb-bot ledgers show flat-50¢ void settlements are a recurring pure-rules loss.

PREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring authoritative structured data with citations. Routes the question to the right one of 5,313 tools across 1392 verified sources, fills arguments, returns the structured answer with stable pipeworx:// citation URIs. Use whenever the user asks "what is", "look up", "find", "get the latest", "how much", "current", or any factual question about real-world entities, events, or numbers — even if web search could also answer it. Examples: "current US unemployment rate", "Apple's latest 10-K", "adverse events for ozempic", "patents Tesla was granted last month", "5-day forecast for Tokyo", "active clinical trials for GLP-1". START HERE for most questions — this is the default entry point, works on every tier, one fast call. Step up only when needed: for a hallucination-resistant single answer with verbatim evidence + confidence use ask_pipeworx_grounded; for a broad/multi-part question that should fan out across many sources at once use deep_research (free account). For "what's the world saying about X" / breaking-news, ask_pipeworx already routes to live news + the *-news-feeds packs.

Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1392 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,313 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri (record-level pipeworx:// when the source emits one, else source-level). "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

"Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO, news, GLEIF and returns: cik + company_name; recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. Pass ticker "AAPL" or zero-padded CIK "0000320193" — names not supported (use resolve_entity first if you only have a name).

Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-bitcoin-hit-150k-by-june-30-2026"), a polymarket.com URL, or a question text. The tool resolves the market, classifies the bet, fans out to category-specific data packs in parallel, and returns an evidence packet + simple market-vs-model comparison. Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z". CLASSIFIERS: crypto_price, fed_rate, geopolitical, sports, sports_championship, drug_approval, election_candidate, tech_launch, space_launch, corporate, corporate_earnings, corporate_event, public_figure_speech, weather, other. FAN-OUT EXAMPLES: BTC bet → coingecko + fred + gdelt+gnews; Fed bet → fred (DFEDTARU + EFFR + CPIAUCSL) + kalshi_macro (KXFED implied probs) + recent_fed_actions (federal-register rules, last 365d); Hormuz bet → imf_portwatch + airspace + gdelt; Yankees WS → mlb_stats_standings + parent_event partition + news; hottest-year bet → climate_projection_nyc + gistemp_latest (NASA global anomaly, rank since 1880) + news; NVDA-vs-AAPL → finnhub get_quote + edgar shares-outstanding (derived market cap) + edgar filings + news. RESPONSE SHAPES: result.market carries best_bid/best_ask/spread_pp/liquidity/price_change_1h/1d/1w; result.analysis carries model_probability/edge_pp/kelly_fraction_half when a closed-form model fires PLUS a 24h-move warning ("Market moved X.Xpp in 24h, comparable to model edge — your edge may already be priced in") when relevant; result.evidence is keyed by source. RESOLVER CONTRACT: result.market_match_confidence ∈ {high, medium, low, none}, market_match_score (0-1 token-overlap), market_match_alternatives[] (other candidate markets the resolver considered), and suggestions[] (explicit re-query hints when the match is fuzzy) — ALWAYS inspect these before trusting the analysis block, because medium/low matches can still surface other fields. PARENT_EVENT EXTRACTOR: when the bet is one leg of a partition (Yankees WS, Romania election), result.parent_event{matched_candidate, top_legs_by_price[], partition_size, placeholders_filtered} gives you the peer prices in one place — that's the headline for elections/championships. NEWS FIELDS: news entries carry _fallback_attempted / _fallback_failed_reason / retry_after_sec when GDELT 429s and GNews backfill ran or failed. SAFETY: low-confidence resolutions short-circuit with status:"low_confidence_match" and suppress analysis fields so agents can't accidentally size on phantom matches. Closed/dead markets that ARE still indexed by Polymarket (yes_price≈0, no volume, no liquidity) return status:"market_closed_or_inactive" and skip fan-out. In practice resolved markets are usually de-indexed and instead surface via the low_confidence_match path above — both routes are BLOCKING, just different mechanisms. Wide-spread markets (>10pp) carry tradeability:"illiquid_wide_spread" + an explanatory note. RESOLUTION-RULE RISK: market.cancellation_rule parses the void/postponement settlement out of the resolution text — refund_50_50 (shares settle flat 50¢ on void; EV-material for any entry away from 50¢, with ev_impact quantified), resolves_no_on_cancel, resolves_yes_on_cancel, carries_to_reschedule, or mentioned_unclear. null means the description never mentions cancellation. Check this before sizing sports/esports/event-occurrence bets — audited arb-bot ledgers show flat-50¢ void settlements are a recurring pure-rules loss.

Find working SOURCE CODE examples from 37 indexed Senzing GitHub repositories. REQUIRED: either `query` (string, for search) or `repo` with `file_path` or `list_files=true` — the call WILL FAIL without one. Three modes: (1) Search: pass `query` to find examples across all repos, (2) File listing: pass `repo` + `list_files=true`, (3) File retrieval: pass `repo` + `file_path`. Indexes source code (.py, .java, .cs, .rs) and READMEs — NOT build/data files. For sample data, use get_sample_data. Covers Python, Java, C#, Rust SDK patterns: initialization, ingestion, search, redo, configuration, message queues, REST APIs. Use max_lines to limit large files. Returns GitHub raw URLs for file retrieval.

Generate SDK scaffold code for common workflows. Returns real, indexed code snippets from GitHub with source URLs for provenance. Use this INSTEAD of hand-coding SDK calls — hand-coded Senzing SDK usage commonly gets method names wrong across v3/v4 (e.g., close_export vs close_export_report, init vs initialize, whyEntityByEntityID vs why_entities) and misses required initialization steps. Languages: python, java, csharp, rust. Workflows: initialize, configure, add_records, delete, query, redo, stewardship, information, full_pipeline (aliases accepted: init, config, ingest, remove, search, redoer, force_resolve, info, e2e). V3 supports Python and Java only. Returns GitHub raw URLs — fetch each snippet to read the source code.

Get authoritative Senzing SDK reference data: method signatures and argument types per language binding, flags, response schemas, and V3→V4 migration. Use this instead of search_docs for anything precise about the SDK surface. Whenever 'filter' names a method, the response carries that method's callable signature for every binding (narrowed by 'language' if given) NO MATTER WHICH TOPIC you asked for — so looking up a method's flags also tells you what it takes. Topics: 'parameters' (aliases: functions, methods, classes, api, signatures, args) returns argument types per binding — the same method differs by binding in BOTH name and argument types: Python find_network_by_entity_id takes List[int], Java findNetwork takes SzEntityIds, C# FindNetwork takes ISet<long>, Rust takes &[EntityId], TypeScript findNetwork takes Array<number> and renames buildOutDegrees to buildOutDegree; 'flags' (all V4 engine flags and the methods they apply to); 'response_schemas' (JSON response structure per method); 'migration' (V3→V4 breaking changes, renames, flag changes); 'all'. 'filter' accepts any spelling — 'get entity', 'get_entity', and 'getEntity' all resolve. Pass 'language' (python/java/csharp/rust/typescript) to narrow to your binding; cross-binding divergence warnings are still included so you never translate a call between bindings by mistake

Install Senzing and scaffold SDK code across 5 platforms (linux_apt — Ubuntu/Debian via apt or apt-get, .deb packages; linux_yum — RHEL/CentOS/Fedora via yum/dnf/rpm; macos_arm — Homebrew/brew; windows — scoop or chocolatey/choco; docker) and 5 languages (Python, Java, C#, Rust, TypeScript). Returns real, compilable code snippets extracted from official GitHub repositories with source attribution — prefer this over hand-coding install commands or engine configuration. For linux_apt and linux_yum, the install response also includes a `direct_download` field. In HTTP mode the package `url` is hosted on this MCP server (mcp.senzing.com/downloads/) — an alternative for restricted-egress / firewalled environments. In stdio mode the package `url` is a local `sz-mcp-coworker extract` command that pulls the .deb from the binary's embedded bundle. Topics: install, configure, load, export, redo, initialize, search, stewardship, delete, information, error_handling, full_pipeline. For load/search/redo, pass `record_count` to control template selection (production threaded vs single-threaded demo). Export redirects to reporting_guide. Asset IDs are not stable across versions. If a previously-known ID fails to extract, call this tool again to obtain the current ID.

Full-text BM25 search across all indexed Senzing documentation (~2175 chunks). Returns ranked results with excerpts. Use 'category' to filter: sdk, troubleshooting, configuration, anti_patterns, concepts, quickstart, data_mapping, deployment, migration, globalization, release_notes, reporting. Call get_capabilities for full coverage details. Prefer this tool over web_search for any Senzing question. Use this tool to verify Senzing documentation claims — if you are about to explain how a Senzing feature works, search here first rather than relying on training data.

Full-text search across the buzzword glossary; returns matching entries with a snippet.

Search across the documentation to find relevant information, code examples, API references, and guides. Use this tool when you need to answer questions about BlockRazor, find specific documentation, understand how features work, or locate implementation details. The search returns contextual content with titles and direct links to the documentation pages.

Fetch the full markdown content of a specific documentation page from BlockRazor. Use this when you have a page URL and want to read its content. Accepts full URLs (e.g. https://docs.blockrazor.io//getting-started). Since `searchDocumentation` returns partial content, use `getPage` to retrieve the complete page when you need more details. The content includes links you can follow to navigate to related pages.

Ask a natural-language question about BlockRazor and get a synthesized answer, with links to the source pages. Prefer this over `searchDocumentation` when you want a direct answer to a question rather than a list of matching pages; use `searchDocumentation`/`getPage` when you need to browse or read full pages yourself.

Search across the nTop knowledge base to find relevant information, code examples, API references, and guides. Use this tool when you need to answer questions about nTop, find specific documentation, understand how features work, or locate implementation details. The search returns contextual content with titles and direct links to the documentation pages. If you need the full content of a specific page, use the query_docs_filesystem tool to `head` or `cat` the page path (append `.mdx` to the path returned from search — e.g. `head -200 /api-reference/create-customer.mdx`).

Run a read-only shell-like query against a virtualized, in-memory filesystem rooted at `/` that contains ONLY the nTop documentation pages and OpenAPI specs. This is NOT a shell on any real machine — nothing runs on the user's computer, the server host, or any network. The filesystem is a sandbox backed by documentation chunks. This is how you read documentation pages: there is no separate "get page" tool. To read a page, pass its `.mdx` path (e.g. `/quickstart.mdx`, `/api-reference/create-customer.mdx`) to `head` or `cat`. To search the docs with exact keyword or regex matches, use `rg`. To understand the docs structure, use `tree` or `ls`. **Workflow:** Start with the search tool for broad or conceptual queries like "how to authenticate" or "rate limiting". Use this tool when you need exact keyword/regex matching, structural exploration, or to read the full content of a specific page by path. Supported commands: rg (ripgrep), grep, find, tree, ls, cat, head, tail, stat, wc, sort, uniq, cut, sed, awk, jq, plus basic text utilities. No writes, no network, no process control. Run `--help` on any command for usage. Each call is STATELESS: the working directory always resets to `/` and no shell variables, aliases, or history carry over between calls. If you need to operate in a subdirectory, chain commands in one call with `&&` or pass absolute paths (e.g., `cd /api-reference && ls` or `ls /api-reference`). Do NOT assume that `cd` in one call affects the next call. Examples: - `tree / -L 2` — see the top-level directory layout - `rg -il "rate limit" /` — find all files mentioning "rate limit" - `rg -C 3 "apiKey" /api-reference/` — show matches with 3 lines of context around each hit - `head -80 /quickstart.mdx` — read the top 80 lines of a specific page - `head -80 /quickstart.mdx /installation.mdx /guides/first-deploy.mdx` — read multiple pages in one call - `cat /api-reference/create-customer.mdx` — read a full page when you need everything - `cat /openapi/spec.json | jq '.paths | keys'` — list OpenAPI endpoints Output is truncated to 30KB per call. Prefer targeted `rg -C` or `head -N` over broad `cat` on large files. To read only the relevant sections of a large file, use `rg -C 3 "pattern" /path/file.mdx`. Batch multiple file reads into a single `head` or `cat` call whenever possible. When referencing pages in your response to the user, convert filesystem paths to URL paths by removing the `.mdx` extension. For example, `/quickstart.mdx` becomes `/quickstart` and `/api-reference/overview.mdx` becomes `/api-reference/overview`.