Tool search 164,478 tools · 10,067 live servers

List the open-source AI applications hosted and ready to run at AI NetCafé (ainetcafe.com). Each one normally requires local setup (Docker/Python + your own model API key); here they run pre-configured. Use this to find a tool for a task like translating a PDF with formulas intact, generating a PowerPoint file, polishing an academic paper, or running an autonomous research report. Do not call this first when the request already clearly matches compare_models, translate_pdf, deep_research, or make_slides; call that task tool directly. Example — GET https://ainetcafe.com/t/list_apps

Retrieve previously stored memories, optionally filtered by search query and/or project. Call at the start of work on a known project to restore context: why decisions were made, known fixes, preferences. Example — GET https://ainetcafe.com/t/recall?query=<what+to+remember> (needs a workspace/key for durable memory)

Search the live web through a self-hosted SearXNG meta-search (aggregates dozens of engines, no tracking). Returns titles, URLs and snippets. Use when you need current information or sources. Example — GET https://ainetcafe.com/t/web_search?query=latest+MCP+spec

Fetch any public URL and return LLM-ready clean Markdown (rendered via Crawl4AI, handles JS pages). Use after web_search to read a source, or to ingest any page for analysis. Example — GET https://ainetcafe.com/t/fetch_page?url=https://example.com

Create a task that runs on a schedule in our cloud — you do not keep anything running. It only notifies you when the result actually changes. Kinds: watch_page (Watch a web page and report when its content changes); daily_answer (Re-run a web-researched question on a schedule and report when the answer changes); watch_reachability (Track whether a site stays reachable from mainland China); pipeline (Run one of your production lines (create_pipeline) on a schedule; every run leaves a proof-carrying work order). Needs a workspace token (?w=ws_... on your MCP URL) so you can manage it later.

Get the status or result of a job started by deep_research, translate_pdf, or make_slides. Poll every 15-30 seconds until status is "done" or "error". While work is pending, follow retry_after_seconds and next_action; when complete, prefer structured_result when present. Example — GET https://ainetcafe.com/t/check_job?job_id=<id-from-a-job-tool>

Tech and research event signals: emerging technology events bootstrap data from curated research feeds.

Search open global public-procurement opportunities through the canonical Pro route.

Search Google Flights for real-time flight options between two airports on a specific date.

Search Google Flights date-grid pricing across a date range.

Semantic search over WorldMonitor's accumulating store of past intelligence events (Pro), ranked by similarity.

Fetch the exact schema(s) for tool(s) you ALREADY know by name — e.g. names a skill/playbook gave you (getTestReportFull, updateTestCase, listTestReports, getMock, getTestCase, getApp, listApps, ...). Returns ONLY the named tools: no fuzzy matching, no unrelated tools. Use this instead of search_tools whenever you know the name — it is exact and cheap. Use search_tools only to DISCOVER names you do not already know. Returns {"tools": [{name, description, inputSchema}, ...], "not_found": [names...]}.

Run any Keploy tool by name. Most tools are hidden from the default list. Know the name from a skill? get its schema with get_tool_schema; don't know the name? discover it with search_tools. Then call invoke_tool with that name and an "arguments" object matching the tool's inputSchema. Example: invoke_tool {"name": "getTestReportFull", "arguments": {"appId": "...", "testRunId": "...", "fields": ["status"]}}

DISCOVER tool names you do NOT already know, by keyword. Most Keploy tools are hidden from the default tool list to save context. If you ALREADY know the exact name (e.g. a skill named it), call get_tool_schema instead — it is exact and far cheaper than this fuzzy search. Returns {"matches": [{name, description, inputSchema}, ...], "total_catalog": N}. Search by intent words, e.g. "test report", "mock patch", "update test case", "cloud replay branch", "record".

Search the web for any topic and get clean, ready-to-use content. Best for: Finding current information, news, facts, people, companies, or answering questions about any topic. Returns: Clean text content from top search results. Query tips: describe the ideal page, not keywords. "blog post comparing React and Vue performance" not "React vs Vue". Use category:people / category:company to search through Linkedin profiles / companies respectively. If highlights are insufficient, follow up with web_fetch_exa on the best URLs.

Read a webpage's full content as clean markdown. Use after web_search_exa when highlights are insufficient or to read any URL. Best for: Extracting full content from known URLs. Batch multiple URLs in one call. Returns: Clean text content and metadata from the page(s).

Use for broad RevoGrid questions or when the exact API name is unknown. Searches source-grounded docs and API evidence; defaults to public exports.

Use for broad RevoGrid questions or when the exact API name is unknown. Searches source-grounded docs and API evidence; defaults to public exports.

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.

Search documentation with hybrid semantic (vector) and keyword (BM25) search. Use semanticWeight to choose keyword-only (0), semantic-only (1), or a blend; mid values fuse rankings with RRF. Supports Tiger Cloud (TimescaleDB), PostgreSQL, and PostGIS.

Retrieve detailed skills for TimescaleDB operations and best practices. ## Available Skills <available_skills> [9 ]{name description}: design-postgis-tables Comprehensive PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based applications design-postgres-tables "Use this skill for general PostgreSQL table design.\n\n**Trigger when user asks to:**\n- Design PostgreSQL tables, schemas, or data models when creating new tables and when modifying existing ones.\n- Choose data types, constraints, or indexes for PostgreSQL\n- Create user tables, order tables, reference tables, or JSONB schemas\n- Understand PostgreSQL best practices for normalization, constraints, or indexing\n- Design update-heavy, upsert-heavy, or OLTP-style tables\n\n\n**Keywords:** PostgreSQL schema, table design, data types, PRIMARY KEY, FOREIGN KEY, indexes, B-tree, GIN, JSONB, constraints, normalization, identity columns, partitioning, row-level security\n\nComprehensive reference covering data types, indexing strategies, constraints, JSONB patterns, partitioning, and PostgreSQL-specific best practices.\n" find-hypertable-candidates "Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables.\n\n**Trigger when user asks to:**\n- Analyze database tables for hypertable conversion potential\n- Identify time-series or event tables in an existing schema\n- Evaluate if a table would benefit from Timescale/TimescaleDB\n- Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData\n- Score or rank tables for hypertable candidacy\n\n\n**Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables\n\nProvides SQL queries to analyze table statistics, index patterns, and query patterns. Includes scoring criteria (8+ points = good candidate) and pattern recognition for IoT, events, transactions, and sequential data.\n" migrate-postgres-tables-to-hypertables "Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation.\n\n**Trigger when user asks to:**\n- Migrate or convert PostgreSQL tables to hypertables\n- Execute hypertable migration with minimal downtime\n- Plan blue-green migration for large tables\n- Validate hypertable migration success\n- Configure compression after migration\n\n**Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed)\n\n**Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup\n\nStep-by-step migration planning including: partition column selection, chunk interval calculation, PK/constraint handling, migration execution (in-place vs blue-green), and performance validation queries.\n" pgvector-semantic-search "Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search.\n\n**Trigger when user asks to:**\n- Store or search vector embeddings in PostgreSQL\n- Set up semantic search, similarity search, or nearest neighbor search\n- Create HNSW or IVFFlat indexes for vectors\n- Implement RAG (Retrieval Augmented Generation) with PostgreSQL\n- Optimize pgvector performance, recall, or memory usage\n- Use binary quantization for large vector datasets\n\n**Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search\n\nCovers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.\n" postgres "Use this skill for any PostgreSQL database work — table design, indexing, data types, constraints, extensions (pgvector, PostGIS, TimescaleDB), search, and migrations.\n\n**Trigger when user asks to:**\n- Design or modify PostgreSQL tables, schemas, or data models\n- Choose data types, constraints, indexes, or partitioning strategies\n- Work with pgvector embeddings, semantic search, or RAG\n- Set up full-text search, hybrid search, or BM25 ranking\n- Use PostGIS for spatial/geographic data\n- Set up TimescaleDB hypertables for time-series data\n- Migrate tables to hypertables or evaluate migration candidates\n- Plan or execute safe schema migrations with zero downtime\n\n**Keywords:** PostgreSQL, Postgres, SQL, schema, table design, indexes, constraints, pgvector, PostGIS, TimescaleDB, hypertable, semantic search, hybrid search, BM25, time-series, migration\n" postgres-database-migration "Use this skill for planning, testing, and safely executing PostgreSQL schema migrations — especially when working with production data or shared databases.\n\n**Trigger when user asks to:**\n- Test a schema migration before applying it to production\n- Add, remove, or rename columns safely on a live table\n- Change a column's data type without downtime\n- Add or drop indexes, constraints, or foreign keys on large tables\n- Understand which ALTER TABLE operations lock the table\n- Roll back a failed migration\n- Plan a zero-downtime migration strategy\n- Fork a database to test a migration safely\n\n**Keywords:** migration, schema change, ALTER TABLE, add column, drop column, rename column, change type, zero downtime, lock, AccessExclusiveLock, concurrent index, forking, rollback, backfill, deploy\n\nCovers: lock-level reference for every common DDL operation, safe migration patterns, fork-based testing, zero-downtime column changes, index creation, constraint addition, backfill strategies, pre/post-migration validation, and rollback planning.\n" postgres-hybrid-text-search "Use this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF).\n\n**Trigger when user asks to:**\n- Combine keyword and semantic search\n- Implement hybrid search or multi-modal retrieval\n- Use BM25/pg_textsearch with pgvector together\n- Implement RRF (Reciprocal Rank Fusion) for search\n- Build search that handles both exact terms and meaning\n\n\n**Keywords:** hybrid search, BM25, pg_textsearch, RRF, reciprocal rank fusion, keyword search, full-text search, reranking, cross-encoder\n\nCovers: pg_textsearch BM25 index setup, parallel query patterns, client-side RRF fusion (Python/TypeScript), weighting strategies, and optional ML reranking.\n" setup-timescaledb-hypertables "Use this skill when creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table.\n\n**Trigger when user asks to:**\n- Create or design SQL schemas/tables AND Timescale/TimescaleDB/TigerData/Tiger Cloud is available\n- Set up hypertables, compression, retention policies, or continuous aggregates\n- Configure partition columns, segment_by, order_by, or chunk intervals\n- Optimize time-series database performance or storage\n- Create tables for sensors, metrics, telemetry, events, or transaction logs\n\n**Keywords:** CREATE TABLE, hypertable, Timescale, TimescaleDB, time-series, IoT, metrics, sensor data, compression policy, continuous aggregates, columnstore, retention policy, chunk interval, segment_by, order_by\n\nStep-by-step instructions for hypertable creation, column selection, compression policies, retention, continuous aggregates, and indexes.\n" </available_skills>

Search the apps directory for small, useful tools, by name, description or tag

Search everything this instance has collected, across news, video, markets and posts at once. Use it when the question crosses a service, or when you do not know which service would hold the answer; news_search is narrower and better when you know it is news

Find your saved reading by text or kind, newest first. Private notes are searched too

List your documents, most recently changed first, with an optional search over titles and bodies. Use this to find an id

Find packaged foods by name or brand, with the barcode of each so one can then be looked up in full

Search the public image library by description and get URLs to reuse. Cheaper than generating: look here first

Full-text search across all published tax and accounting skills. Find, lookup, query, or discover skills by keyword, tax concept, deduction type, form number, or regulation (e.g. 'home office deduction', 'crypto capital gains', 'reverse charge', 'Schedule C', '60-day reporting'). Optionally limit to one jurisdiction. Use this when you don't know the exact skill slug. Set content_type='method' to discover step-by-step METHODS (guided procedures — what you EXECUTE via start()) instead of reference material; each method carries a review_status (accountant-reviewed | source-cited).

Sweep tax rules ACROSS jurisdictions and metadata and get back a bundled markdown rule set plus the jurisdiction's OFFICIAL SOURCE. Best for cross-cutting questions — 'VAT rates in MT, IE and DE', 'all income-tax thresholds for 2025', 'rules mentioning reverse charge'. WHEN YOU WANT ONE SPECIFIC FIGURE for one jurisdiction (a single rate, threshold or date), prefer `search_skills` then `get_skill`: the Guide is where a topic is covered in full, and a figure can exist in the Guide without existing as a separate rule here, in which case this tool will return related guidance rather than the number you asked for. Entries whose text is guidance rather than a value are tagged `GUIDANCE` — never read one as a figure. IMPORTANT: this returns human-reviewed GUIDANCE and does NOT certify that a figure is current — confirm each number against the official source returned in `official_source` before you state it, and prefer that source when they disagree. By default returns ALL matching rules, each tagged with its verification status; pass status:'verified' for accountant-/research-verified only. Call `list_rule_facets` first to see the queryable values.

Returns the metadata you can filter on with `search_rules` — the live jurisdictions, the domains, roles, block types (rule kinds), statuses, tax years, and a sample of topics — plus the defaults. Call this before `search_rules` to learn the valid filter values rather than guessing.

Semantic search across fojin's Buddhist corpus (10K+ texts, 30+ langs). Returns the most relevant passages, each with a `urn`, title, snippet and similarity score. `lang` optionally filters by language code (lzh=Classical Chinese, pi=Pali, sa=Sanskrit, bo=Tibetan, en=English).

Read the full content of one fascicle (卷) of a text, with its `urn`. Use the `text_id`/`juan_num` from a search_corpus hit. Returns the actual canonical text — quote from this, not from memory.

Search fojin's knowledge graph for entities — people, places, works, doctrinal terms — matching `query`.

Verify that a Buddhist-canon quote exists VERBATIM in the corpus. Call this before presenting any quoted scripture to a reader: LLMs routinely invent plausible-looking quotes. Returns `verbatim` (bool), where it was found (`matches`, each with a resolvable `urn`), or the closest near-miss window when it wasn't. `cite` optionally narrows the search — a CBETA id ("T0374") or fojin URN ("fojin:cbeta/T0374.13") — and `cite_matched` reports honestly whether the quote is where you claimed (a hit in a different fascicle does NOT confirm your citation). Quote must be ≥4 CJK chars after normalisation; Classical Chinese only. Short quotes are answered but say less: a four-character phrase recurs across the canon, so read `cite_matched` rather than `verbatim`, and check `matches_capped` before treating the list as complete. Each match carries an absolute `reader_url` — cite that, not a reconstructed third-party link.

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 published agent-asset exchange by task or capability.

Search the SubmitMap directory of startup launch platforms and directories. Filter by free-text query, category, pricing, link type, backlink requirement, approval speed or domain rating, and with a token leave out the ones this project has already been sent to. This is the tool behind "find me a small directory I can submit to right now": it is the whole directory, so answer from it rather than from what you remember of the web. Returns summaries; call get_platform for the full record including the submission steps.

Search read-only HeyClaude registry entries by query, category, exact tag, and skill platform compatibility.

Answer 'what should I use to do X' in one call. Given a plain-language task (and optional platform/category), returns the best-match HeyClaude entries ranked by fit — each with why it fits, trust summary, disclosed safety/privacy notes, and an inline install block — plus a topPick and a consolidated installPlan. Unlike workflow.plan it does not force category diversity; it returns the genuinely best matches. Collapses the search → compare → detail → asset loop into a single answer-shaped response.

Search generated registry artifacts for likely duplicate entries before a user opens a submission PR.

Browse the catalog of 260+ audit rules that run during an audit, grouped into categories (crawlability, meta tags, performance, security, accessibility, content, and more). Filter by category code or search by keyword to find what a specific rule checks. Use get_rule for one rule's full detail.

Keyword search over the COTAL build log (blog posts) and the key documentation and site pages. Returns ranked results with canonical and markdown URLs. Use it to find the page that answers a question before reading it.

Returns one build-log post as markdown (title, summary, date, author, full body) by its slug. Get slugs from cotal_list_posts or cotal_search.

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.

Search tennis players by name. Returns id, country, ranking and tour. Use the returned id with get_player.

Search the tournament catalogue — the stable id space that match objects carry as tournament_id. Returns surface, indoor, host city/country and category where curated.

Search the results archive — completed-match RESULTS from 1968 through 2022: ATP and WTA, main draws, qualifying and the ITF/futures tiers. Winner/loser-shaped records with final score, seeds and ranks AT THE TIME of the match. Use this for historical questions ("Borg's Wimbledon finals"); the archive ends 2022-12-31 where our own results (get_recent_results) begin. Requires the BASIC plan or any History plan.

The people of the results archive (1968–2022): hand, date of birth, country, height, and career-high rank with the week it was first reached. Their ids are corpus person ids (the winner/loser player_id on archive results), not roster ids — for current players use search_players. Requires the BASIC plan or any History plan.

Interactive dashboard variant of `search`: returns summary metrics, a source breakdown chart, and a results table with a click-to-expand detail panel, all rendered in the conversation. Same params as `search`. Use for a richer visual overview of a query's results. Automatically selects an engine-specific dashboard when available (e.g. google_flights gets price intelligence charting).

Interactive UI variant of `search`: returns organic results as a sortable, searchable table rendered in the conversation. Same params as `search`. Use when the host supports MCP Apps and the user wants to browse results visually rather than read JSON.

Universal search tool supporting all SerpApi engines and result types. When to use: - Any query needing live, structured SERP data: web results, news, product listings, job postings, local businesses, flight/hotel prices, video results, images, stock/weather cards, knowledge graph entities. Engine discovery via MCP resources: - serpapi://engines lists all engines supported by this tool. - serpapi://engines/<engine> provides engine-specific parameters and supported options. - Example: serpapi://engines/google_news Input schema: params: JSON object containing SerpApi engine parameters. Common parameters: - q: Search query. Required for most engines. - engine: SerpApi engine name. Defaults to "google_light". - location: Optional geographic location for localized results. - output: Optional response format. Omit for JSON (default), or set to "md" for Markdown. Engine-specific parameters are available via MCP resources: - serpapi://engines lists all supported engines. - serpapi://engines/<engine> provides parameters and options for one engine. mode: Response mode. Defaults to "complete". - "complete": Return the full SerpApi response. - "compact": Remove metadata fields from JSON responses. Markdown is returned unchanged. Output schema: Markdown when params.output is "md"; otherwise a JSON string or an error message. Examples: Weather: {"params": {"q": "weather in London", "engine": "google"}, "mode": "complete"} Stock: {"params": {"q": "AAPL stock", "engine": "google"}, "mode": "complete"} General: {"params": {"q": "coffee shops", "engine": "google_light", "location": "Austin, TX"}, "mode": "complete"} Compact: {"params": {"q": "news"}, "mode": "compact"} Markdown: {"params": {"q": "news", "output": "md"}} Supported engines include (not limited to): - google - google_light - google_flights - google_hotels - google_images - google_news - google_local - google_shopping - google_jobs - bing - yahoo - duckduckgo - youtube_search - baidu - ebay

Search PubMed with full query syntax, filters, and date ranges. Returns PMIDs and optional brief summaries. Supports field-specific filters (author, journal, MeSH terms), common filters (language, species, free full text), and pagination via offset for paging through large result sets.

Spell-check a query and get NCBI's suggested correction. Useful for refining search queries.

Search and explore the MeSH (Medical Subject Headings) controlled vocabulary. Returns descriptor records with tree numbers, scope notes, and entry terms, plus pagination via offset for paging past the maxResults cap.

Look up PubMed IDs from partial bibliographic citations. Useful when you have a reference (journal, year, volume, page, author) and need the PMID — deterministic citation matching, more reliable than free-text search for structured references. Each citation must include at least journal or year (ECitMatch primary-keys on journal+volume+page; author-only or volume-only inputs guarantee no match); more fields = better match accuracy.

Convert between article identifiers (DOI, PMID, PMCID). Accepts up to 50 IDs of a single type per request. Only resolves articles indexed in PubMed Central — for articles not in PMC, use pubmed_search_articles instead.

Search Europe PMC, a broad open-access biomedical corpus. Surfaces preprints (`source: PPR`), patents (`source: PAT`), Agricola (`source: AGR`), plus everything in PubMed (`MED`) and PMC. Use when additional coverage is needed — preprints and EPMC-only OA records are the typical recovery. Paginate via `cursorMark`. Defaults to `MED`, `PMC`, and `PPR`; pass `sources` to include `PAT` / `AGR`. Abstracts arrive as a bounded `abstractSnippet` with `abstractTruncated` marking the cut ones — pass a hit’s `source` and `epmcId` to `pubmed_europepmc_fetch` for the complete abstract.

Semantic retrieval (#1089): return the records in a CSV most similar to a free-text query, ranked by cosine similarity. Embeds the chosen column and the query with the zero-config in-house embedder (no cloud/torch by default) and runs ANN search. The read side of the RAG entity-canonicalization epic -- fetch candidate records by query without running a full dedupe.

Find duplicate matches for a record. Provide field values to search against the loaded dataset.

Alias for `find_duplicates`. Find duplicate matches for a record. Provide field values to search against the loaded dataset.

Search the immutable Sourcey catalog.

Search the web based on a provided query. 'return_content' is used to return markdown content for each search result. If 'return_content' is set to True, you don't need to use ai_scraper to get the content of the search results urls, because it is already included in the search results. if 'return_content' is set to True, prefer lower 'limit' to reduce payload size.

Scrape Google Search results. Supports content parsing, different user agent types, pagination, domain, geolocation, locale parameters and different output formats.

Scrape Amazon search results. Supports content parsing, different user agent types, pagination, domain, geolocation, locale parameters and different output formats. Supports Amazon specific parameters such as category id, merchant id, currency.

Search for clinical trial studies from ClinicalTrials.gov. Supports full-text and field-specific queries, status/phase/geographic filters, pagination, sorting, and field selection. Returns a compact per-study index by default; pass the fields parameter to get specific leaves at full fidelity — full study records are ~70KB each.

Discover valid values for ClinicalTrials.gov fields with study counts per value. Use to explore available filter options before building a search — e.g., valid OverallStatus, Phase, InterventionType, StudyType, or LeadSponsorClass values.

Resolve valid field names from the ClinicalTrials.gov data model — the canonical PascalCase identifiers (OverallStatus, EnrollmentCount, LeadSponsorName) accepted by the `fields`, `advancedFilter`, and `sort` parameters of other tools, and as input to clinicaltrials_get_field_values. Select a mode: `"search"` — keyword search returning ranked matches (pass `query`, e.g. "enrollment", "sponsor", "adverse events"); `"drill"` — drill into a specific section by dot-notation path (pass `path`, e.g. "protocolSection.designModule"); `"overview"` — top-level summary of all sections (no additional args).

Fetch clinical trial results data from ClinicalTrials.gov for completed studies — outcome measures with statistics, adverse events, participant flow, baseline characteristics, and results metadata (limitations & caveats, certain-agreement disclosure restrictions, results point of contact). Only available for studies where hasResults is true. Use clinicaltrials_search_studies first to find studies with results. A results-rich record can exceed 500KB per study in full mode — bound it with summary=true, narrower sections, or the outcomeLimit / adverseEventLimit caps, whose trims are reported per study in filtersApplied.

Match patient demographics and conditions to eligible recruiting clinical trials. Provide age, sex, conditions, and location to find studies with matching eligibility criteria, contact information, and recruiting locations. Results are re-ranked so studies whose own condition matches a requested condition surface above tangential matches from ClinicalTrials.gov's fuzzy condition search. Each candidate returns only the sites matching the requested location (capped by locationLimit), not the study's full registered site list — a large trial can register hundreds of sites worldwide. When none of a candidate's matched sites is recruiting, its nearest recruiting site is added, so an enrollable site is never hidden behind a closer closed one. Fetch a study's complete record with clinicaltrials_get_study_record.

Write conversation messages to DeepMemory for fact extraction and persistent storage. Messages are processed by an LLM to extract structured memories, which are then embedded and stored in a vector database for later semantic search. Set infer=True to enable LLM fact extraction (produces richer memories but costs one LLM call). Set infer=False to store raw messages without extraction. Returns a list of memory IDs for successfully stored facts.

Search memories stored in DeepMemory using semantic search. Returns the most relevant memories for the given query, ranked by hybrid scoring (vector similarity + BM25 keyword match + entity boost + time decay). Use this to retrieve context from past conversations before responding to the user. Memories are scoped to the user_id provided during write.

Full-text search within one channel. Use this instead of paging history when you know roughly what was said. Same access rules as freeq_history.

Find runx skills by what they do. Returns each match with its skill id, owner, description, latest version, and trust tier, so a caller can pick one and then read it in full with get_skill.

Search detailed documentation for Strudel live coding or ABC/ABCJS notation. Returns relevant code examples and explanations from the official docs. Use this when the curated guides (get-strudel-guide, get-music-guide) don't cover what you need — for specific functions, advanced techniques, or when you're unsure about syntax. Powered by semantic search over strudel.cc and ABCJS docs.

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.