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

Search all Telegram chats at once (not scoped to one chat). Comma-separated query terms; optional filters by date, chat kind, and public username. Success: message list and metadata dict. Global search ignores include_total_count. Full documentation: https://github.com/alexeyleshchenko/fast-mcp-telegram/blob/main/docs/Tools-Reference.md

Read or search messages in one chat: browse latest, search text, fetch by ids, or load replies to a message (comments, forum topics, threads). Use from_user to filter by sender (server-side, per-chat only). Use context to include neighboring messages and reply chains around each result. Use include_replies to fetch up to 5 direct replies per result. Do not combine message_ids with query or reply_to_id. Success: messages, has_more, optional total_count and discussion fields. Full documentation: https://github.com/alexeyleshchenko/fast-mcp-telegram/blob/main/docs/Tools-Reference.md

Find users/groups/channels by name, username, or phone. Comma-separated usernames are searched in parallel and results are merged round-robin. Global search (query required) searches all Telegram; with min_date, max_date, or filter, search uses dialog list or a named filter; include_peers filters use last-activity from GetPeerDialogs; flag-based filters use dialog list dates. Success: dict with key chats (list of chat objects). Full documentation: https://github.com/alexeyleshchenko/fast-mcp-telegram/blob/main/docs/Tools-Reference.md

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

Search for tools using natural language. Returns matching tool definitions ranked by relevance, in the same format as list_tools.

Call a tool by name with the given arguments. Use this to execute tools discovered via search_tools.

List and search your uploaded datasets — fuzzy matching on name, description, and tags. Returns each dataset's uuid:// reference for use in create_analysis and run_analysis.

Your report library — every analysis delivered, with status and links. Pass semantic_query to search report content in plain language.

Search Twitter/X. Returns a compact human-readable list by default; pass format='json' for full structured data. Use glim_twitter_get(ref) for full thread context. Use docs://search-operators for reference.

Search Reddit posts. Each result comes with full post content and its top comments, so a single search usually answers the question without follow-up. Compact human-readable text by default; pass format='json' for full structured data. Use glim_reddit_get(ref) for a single post's complete comment tree. Page with cursor (response gives next_cursor when more exist). See docs://reddit-search.

Semantic web search powered by Exa. Returns titles, URLs, and the top query-relevant excerpt per result. Compact text by default; pass format='json' for full structured data incl. all excerpts per result. Use glim_web_fetch(url) for full page content. Matching is semantic, so a query with no real match still returns ten nearest-neighbour results rather than zero - judge relevance from the excerpts, not from the result count.

Fetch a single web page and extract clean content. Auto-tier server-side: handles SSR (Next.js, Nuxt, TikTok, Pinterest, YouTube), SPA shells, PDFs, paywall detection, residential-proxy escalation, and stealth profiles for TikTok / Instagram / Pinterest / YouTube. Returns clean markdown (default) with a YAML frontmatter header (url, outcome, total_chars). Read 'outcome' to classify the result (success | teaser | thin_content | paywall | bot_challenge | consent_wall | login_wall | rate_limited | timeout | transient_upstream | unsupported_target | not_found | error). Large pages (>80k chars) are truncated inline with truncated_chars + a download_full_url to the complete extraction (expires ~1h). Permanently unsupported (outcome=unsupported_target, cost=0 upstream): Bluesky search, Instagram post/reel and tag/explore pages (profiles work), Pinterest search, g2.com, Truth Social, Xiaohongshu. Threads and Instagram profile pages ARE supported.

Search GitHub repositories, conversations (issues+PRs), discussions, or code, with full GitHub search syntax in the query: qualifiers (repo:, org:/user:, language:, path:, symbol:, content:, is:, stars:, label:, sort:stars), boolean AND/OR/NOT with parentheses, "exact strings", and /regex/. kind='repos': MINIMAL distinctive keywords - the project/library name only ('rtk', 'react query'); every extra word must ALL match and buries the canonical repo - filter with qualifiers, not prose. kind='code': ONE literal code pattern as it appears in files ('useState('), an "exact string", a /regex/, or symbol:name to find definitions, across 2.8M+ public repos; narrow with repo:/language:/path:. Not supported in code search: license:, enterprise:, is:vendored, is:generated. kind='conversations': returns compact previews - use glim_github_get for full content; sort: REPLACES relevance ranking (words match anywhere incl. comments), omit it for best matches. kind='discussions': GitHub Discussions, a SEPARATE index from issues/PRs - a question answered there never appears under conversations, so reach for it when a repo does its Q&A in Discussions; supports repo:/org:/author:/is:answered plus category: (the repo's own category name, needs a repo: scope), up to 10 results per page, no sort:. Set repo='owner/name' to scope to one repository (works with any kind; with repos it routes to conversations). kind is optional - inferred from the query (is:answered/category: -> discussions, is:/label: -> conversations, path:/symbol://regex/ -> code, stars:/topic: -> repos, else repos); a conversations search with no matches is retried as discussions and says so. Returns compact text by default; pass format='json' for full structured data.

Pass exactly ONE of {query} or {category_slug}. Searches Amazon (com|co.uk|de|fr|es|it) and returns ranked hits with buybox price (gross + VAT-excluded net), ratings, review counts, and ASINs. Drill down with glim_amazon_get(ref). Set sort_by='most_reviewed' (with min_reviews to filter junk) for a trust-weighted re-rank within the current page. Compact text by default; pass format='json' for full structured data.

Creates a new Study workspace from existing Audiences or inline Audience configurations; it does not ask questions or run research. Follow-up research in an existing Study does not require another Study. Every questionnaire, survey, battery, section, cohesive question set, or request with two or more known questions belongs in one planned and confirmed multi-question block inside that Study. Never submit such a known set as separate direct questions. A direct question is appropriate only when exactly one standalone question is known or the next question depends on earlier results. Composite creation is atomic and rolls back partial Audience failures. Studies are private by default; enabling link sharing also publishes the attached Audiences and Minds.

Submits exactly ONE respondent-visible question in an existing Study. Applicability: one standalone question, or one adaptive follow-up whose wording could not be known before earlier results. Exclusion: never use this operation for a questionnaire, survey, battery, section, cohesive question set, or any request containing two or more known questions—even when every question targets the same Study. The complete known set belongs in one planned and confirmed multi-question block inside the Study and must be submitted once, not question by question. Follow-up questions remain within the existing Study; this operation does not create or enumerate Studies. The full question value may reach respondents and is not planner-only metadata. Scale, categorical, and qualitative questions are classified automatically, and the response includes status and workspace links. Automatic classification may reformulate the question; this operation does not promise verbatim wording. Locked respondent wording and response formats require a reviewed and confirmed Study plan. The question is classified before it is queued, so the returned status is authoritative: queued or running means it was submitted to respondents, while planning_required means it was declined as an unrefined research objective and nothing was submitted. A planning_required response carries the original request plus a proposed headline and respondent questions for study planning. MCP cannot read or upload a local file:// path. Use a fetchable HTTP(S) URL, a signed URL supplied by the client for the attached file, or an existing Minds workspace upload URL/path. Study tools import external file URLs into durable Minds storage before saving or running. The Study refuses to start if Minds cannot read the asset.

Lists the authenticated user's Studies with their Audiences, Minds, status, sharing state, and workspace or shared links. Accepts an optional fuzzy name search. A Study is the persistent workspace that contains its Audiences, questions, multi-question blocks, results, exports, and history.

Lists the authenticated user's Audiences, including member Minds, sharing state, and workspace or shared links. Accepts an optional fuzzy name search.

Supports operationId-only requests to read a previously accepted preview or creation job and retrieve its result without creating another Audience. Creates a grounded Audience of synthetic Minds from a population or audience brief. It can combine authoritative web research, supplied sources, research files, and reviewed spreadsheet distributions; persists provenance and allocation audits; supports balanced, segment-coverage, and benchmark-depth sizing; generates the same member portraits and Audience cover as in-app creation; can individually train every member of a large reviewed-dataset cohort in the background (trainMembers); is idempotent for identical inputs; and keeps the Audience private unless link sharing is enabled.

Creates or revises a non-executing draft for a multi-question plan inside an existing Study. Applicability: this is the setup operation for every questionnaire, survey, battery, section, cohesive question set, or request containing two or more known questions—even when the user did not say “study.” Include every question known now in this ONE draft, group related questions into cohesive named modules (question blocks or batteries), preserve their logical order, and execute them later as one confirmed run inside the Study. Never split a known set across one-question drafts or sequential runs. A one-question draft is valid only for genuinely standalone research; an adaptive follow-up whose wording depends on unavailable results can be planned later. The draft records intent, respondent-visible stimulus and questions, response formats, locale, method, outputs, confirmation questions, execution source policy, and revision metadata. Its source policy is part of the exact revision the user reviews; omitted means the controlled request_only default, while auto is an explicit experiment opt-in. It does not start research. When the user supplies a fixed or pre-registered instrument whose wording, order, and response formats must not change, pass it as questions (one entry per item with its exact response contract) instead of request; the planner is then bypassed and the draft is an exact transcription. Explicit response contracts remain authoritative during execution, including with attachments: categoricalOptions retain their labels and order rather than being replaced by inferred A/B file labels. MCP cannot read or upload a local file:// path. Use a fetchable HTTP(S) URL, a signed URL supplied by the client for the attached file, or an existing Minds workspace upload URL/path. Study tools import external file URLs into durable Minds storage before saving or running. The Study refuses to start if Minds cannot read the asset.

Use this when you need to look up a Twitter/X user profile by username or URL. Returns structured profile data including bio, follower/following counts, tweet count, verification status, and recent activity. 1. username: the @handle 2. displayName: full name 3. bio: profile description text 4. followers: follower count 5. following: following count 6. tweetCount: total tweets posted 7. verified: blue checkmark status 8. createdAt: account creation date 9. avatarUrl: profile picture URL 10. bannerUrl: header image URL 11. location: stated location 12. website: linked URL 13. pinnedTweet: text of pinned tweet if any Example output: { "username": "elonmusk", "displayName": "Elon Musk", "bio": "...", "followers": 195000000, "following": 850, "tweetCount": 45000, "verified": true, "createdAt": "2009-06-02" } Use this FOR social media due diligence, influencer research, competitor monitoring, or verifying the legitimacy of an account before trusting its content. Do NOT use for tweet search -- use twitter_search_tweets instead. Do NOT use for trust/security scoring -- use trust_score_evaluate instead. Do NOT use for email lookup from social -- use email_find_by_name instead.

Use this when you need to find tweets about a topic, brand, event, or keyword. Returns up to 20 recent tweets matching the query with full text, engagement metrics, author info, and timestamps. 1. query: the search term used 2. results: array of tweet objects 3. Each tweet contains: id, text, author (username + displayName), createdAt, likes, retweets, replies, views, url 4. resultCount: number of tweets found Example output: { "query": "x402 protocol", "resultCount": 15, "results": [{ "id": "1234567890", "text": "x402 is the future of agent payments...", "author": { "username": "web3dev", "displayName": "Web3 Dev" }, "likes": 42, "retweets": 12, "replies": 5, "views": 1200, "createdAt": "2026-04-13T09:30:00Z" }] } Use this FOR market sentiment analysis, brand monitoring, competitor tracking, news discovery, trend detection, or finding what people say about a topic in real-time. Do NOT use for profile data -- use twitter_scrape_profile instead. Do NOT use for web search (non-Twitter) -- use web_search_query instead. Do NOT use for sentiment analysis of text -- use text_analyze_sentiment instead. Do NOT use for crypto news -- use crypto_get_news instead.

Use this when you need to see what a specific Twitter/X user has been posting recently. Returns their latest tweets with full text, engagement metrics, and timestamps. 1. username: the @handle queried 2. tweets: array of tweet objects with id, text, createdAt, likes, retweets, replies, views, isRetweet, isReply 3. tweetCount: number of tweets returned Example output: { "username": "VitalikButerin", "tweetCount": 10, "tweets": [{ "id": "...", "text": "Excited about the new EIP proposal...", "likes": 5200, "retweets": 890, "views": 250000, "createdAt": "2026-04-12T14:00:00Z", "isRetweet": false }] } Use this FOR monitoring specific accounts, tracking influencer activity, analyzing posting patterns, or gathering content from thought leaders. Do NOT use for profile bio/stats -- use twitter_scrape_profile instead. Do NOT use for topic search -- use twitter_search_tweets instead. Do NOT use for social profile lookup across platforms -- use social_lookup_profile instead.

Check availability and price for one or more domains. Pass full domain names (e.g. mysite.com). For bulk TLD search, pass an array like ["myapp.com", "myapp.io", "myapp.dev"]. Uses RDAP + retail pricing.

Fast DNS-based domain existence check. Tests if a name is taken across many TLDs at once (faster than search, no pricing). Returns 'taken' (definitely registered) and 'candidates' (potentially available). Use this to narrow down before calling search for pricing. Use preset: 'extended' to check 30+ creative/exotic TLDs when basic ones are all taken.

Buy a taken domain that's listed for sale on an aftermarket (Afternic/Sedo) at its buy-now price, natively - no external site. Use when search shows a domain with for_sale.buyable = true. Always confirm the price with the user first. Pass max_price to cap it. If the listing is make-offer only (not buyable), use acquire_domain (broker) to negotiate instead. Crypto/USDC works like buy_domain (402 -> pay -> retry with payment_tx).

Create a new API token with optional scoped permissions and spend caps. The full key is returned only once - save it immediately. A token can only grant scopes it already has (scope attenuation) and spend caps at or below its own. Scopes: domains:read (GET /api/domains, GET /api/domains/{domain}, GET /api/domains/{domain}/dns, /dnssec, /status, /email/check, /auth-code, /transfer-away, /transfer-status, /analytics), domains:write (PUT /api/domains/{domain}/dns, POST/DELETE /api/domains/{domain}/dnssec, POST /connect, POST /verify, PUT /settings, PUT /parking, PUT/DELETE /api/domains/{domain}/for-sale, POST /api/domains/import, POST /import/verify), domains:transfer (POST /api/domains/buy, POST /transfer, POST /renew (involves payment, includes marketplace purchases)), tokens:read (GET /api/tokens), tokens:write (POST /api/tokens, DELETE /api/tokens/{id}), webhooks:read (GET /api/webhooks, GET /api/webhooks/{id}/deliveries), webhooks:write (POST /api/webhooks, PATCH /api/webhooks/{id}, DELETE /api/webhooks/{id}), email:read (GET /api/emails, /api/emails/{address}, /api/emails/{address}/messages, /api/emails/{address}/aliases, /api/email/changes, /api/domains/{domain}/email/status, /api/domains/{domain}/email/deliverability, /api/suppressions), email:write (POST /api/emails, POST /api/emails/{address}/send, POST /api/domains/{domain}/email/setup, aliases + catch-all, POST/DELETE /api/suppressions), email:delete (Permanently delete messages already in Trash. Moving messages to Trash only requires email:write. Grant this scope only to agents allowed to irreversibly erase email content), email:auth_secrets (Read messages classified as authentication mail (OTP / verification codes, password resets, magic links). Without it, email:read still lists them but subject and body come back redacted - so a stolen agent token can't harvest 2FA codes. Grant it only to agents that genuinely need to complete logins), account:read (GET /api/me, GET /api/agents/identity), account:write (DELETE /api/me, POST /api/billing/setup, POST /api/billing/subscribe, POST /api/billing/cancel, POST /api/me/resend-verification, POST/PATCH/DELETE /api/agents/identity), billing:read (GET /api/billing/invoices), search (GET /api/domains/search, /suggest, /whois, /dns-check, GET /api/tlds), deals:read (GET /api/deals, GET /api/deals/{id}), deals:write (POST /api/domains/sell, PATCH /api/deals/{id}), notifications:read (GET /api/notifications, GET /api/notifications/count), backorders:read (GET /api/backorders, GET /api/backorders/{id}), backorders:write (POST /api/backorders, DELETE /api/backorders/{id}). Use ['*'] for full access (default). When minting a token for a sub-agent that can buy, ALWAYS set max_per_tx/max_per_month.

Add DNS records to verify domain ownership for a third-party service (Stripe, Google Search Console, AWS SES, Postmark, Resend, Facebook, HubSpot, Microsoft 365). Unknown services fall back to a generic TXT record.

List all supported services for domain verification (Stripe, Google Search Console, etc.)

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 5,908 tools across 1540 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 1540 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 5,908 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 — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "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).

"What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (cross-source identity spine: 10-digit CIK + ticker + company_name from SEC EDGAR, legal-entity LEI from GLEIF with parent/ultimate-parent/children ownership when the LEI resolves, and security FIGI from OpenFIGI — by exact ticker map when a ticker is implied, and otherwise by name search, so NON-EQUITY instruments that never have a ticker (municipal and corporate bonds, notes, authority debt) DO resolve here; when a name matches more than one instrument it asserts nothing and returns `figi_candidates` to pick from, which is the correct answer to an issuer name that does not identify a single bond; every identifier is labelled with the source that established it, and an identifier that could NOT be resolved is stated explicitly under `unresolved` rather than omitted — accepts ticker, CIK, ISIN, or company name as input; an ISIN like "CH0038863350" resolves to the LEGAL ENTITY that issued the security via the GLEIF ISIN-to-LEI mapping, covering non-US issuers EDGAR cannot reach), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/concept/{rxcui} citation; accepts brand or generic name). LEI/FIGI enrichment degrades gracefully — if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return. Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.

"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 patents, federal contracts (USAspending), FDA-licensed biologics (Purple Book), H-1B hiring (DOL LCA), news and GLEIF, and returns: cik + company_name (+ resolved_from/resolved_to when `value` was a 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); federal_contracts (USAspending awards where the company is the recipient); fda_products (FDA-licensed biologics — vaccines, cell/gene therapies — from the Purple Book; a company with only small-molecule/generic drugs will show none here, that is expected, not a failure); hiring (H-1B sponsorship volume + salary range from DOL LCA filings); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. `sources_used` / `sources_failed` say which of these actually returned data for THIS company — an empty section is a real "no data", not a bug. Pass a ticker ("AAPL"), zero-padded CIK ("0000320193"), OR a company name ("Moderna") — names now resolve via SEC EDGAR's company-name match; a private company (no CIK/ticker) returns resolved:false with an explicit `notes` line, not a bare failure. `type` accepts "company" or "ticker" interchangeably — both take the same `value` shapes above.

Fetch one stored value by its exact key. When to use: Call when you know the key you wrote earlier. Cheaper and more precise than memory.search. Price: US$0.000500 per call.

Find stored memories by meaning rather than by exact key. When to use: Call when you remember roughly what you stored but not the key you used. Price: US$0.002000 per call.

createAccount: Create a new OSIR customer account; step 1 of onboarding, no authentication required. The contact must be the PRINCIPAL's real ICANN registrant contact (the human or business the account is for), never the AI agent itself. Sends a verification email; complete via verifyAccount with the emailed code. While PENDING_VERIFICATION the account can search, quote and fund; billable actions need ACTIVE. Calling again for a PENDING account re-sends the verification email.

listContacts: List all contacts for the authenticated user, optionally filtered by a search term. Requires authentication. Returns each contact with its id for use in getContact, updateContact, deleteContact, or domain registration.

Search for people/contacts matching criteria and find their professional email address from name and company domain. Alternative to Apollo people-search at 4x lower cost. Returns the most likely email with confidence score after testing 15+ patterns against MX records. 1. email (string) -- best matching email address found 2. confidence (number 0-100) -- likelihood the email is correct 3. pattern (string) -- the pattern that matched (e.g. "first.last", "flast", "first") 4. allCandidates (array) -- all tested patterns with individual scores 5. domain (string) -- company domain used 6. mxValid (boolean) -- whether domain has valid MX records Example output: {"email":"john.doe@stripe.com","confidence":92,"pattern":"first.last","allCandidates":[{"email":"john.doe@stripe.com","score":92},{"email":"jdoe@stripe.com","score":75},{"email":"john@stripe.com","score":60}],"domain":"stripe.com","mxValid":true} Use this BEFORE sales outreach, cold emailing, or building prospect contact lists. Essential for searching for people/contacts and finding decision-maker emails when you only know their name and company. Drop-in replacement for Apollo people search. Do NOT use for email validation -- use email_verify_address instead. Do NOT use for company data -- use company_enrich_from_domain instead. Do NOT use for person data from email -- use person_enrich_from_email instead.

Search for people/contacts matching criteria and find their professional email address from name and company domain. Alternative to Apollo people-search at 4x lower cost. Returns the most likely email with confidence score after testing 15+ patterns against MX records. POST variant of email_find_by_name -- same params passed as JSON body instead of query string. 1. email (string) -- best matching email address found 2. confidence (number 0-100) -- likelihood the email is correct 3. pattern (string) -- the pattern that matched (e.g. "first.last", "flast", "first") 4. allCandidates (array) -- all tested patterns with individual scores 5. domain (string) -- company domain used 6. mxValid (boolean) -- whether domain has valid MX records Example output: {"email":"john.doe@stripe.com","confidence":92,"pattern":"first.last","allCandidates":[{"email":"john.doe@stripe.com","score":92},{"email":"jdoe@stripe.com","score":75},{"email":"john@stripe.com","score":60}],"domain":"stripe.com","mxValid":true} Use this BEFORE sales outreach, cold emailing, or building prospect contact lists. Essential for searching for people/contacts and finding decision-maker emails when you only know their name and company. Drop-in replacement for Apollo people search. Do NOT use for email validation -- use email_verify_address instead. Do NOT use for company data -- use company_enrich_from_domain instead. Do NOT use for person data from email -- use person_enrich_from_email instead.

Search Sayba community: keyword search, advanced search (filter by type), trending keywords.

Agent memory system: create, list, search, and delete persistent memories. Memories persist across sessions and can be searched by vector similarity. Requires API key.

Skill marketplace: search 2500+ skills across 14 categories, view skill details, invoke skills, publish new skills, rate and review. Search is public; publish/invoke require API key.

Read-only availability and pricing lookup for domain names. No purchase or order is created by this tool; it only returns information. Preferred input: `domains`, 1 to 200 fully-qualified names (e.g. ['acme.com', 'acme.io']); results cover exactly those domains, with no suggestions or expansion. Fallback input: `query`, free text (one or more names, comma- or space-separated); names given without a TLD are expanded to popular TLDs (com/io/ai/co/net). Each result includes whether the domain is available, whether it is a premium name, the registration price and the renewal price. Both prices are totals for one full registration term of that ending, not per-year rates: one year on most endings, but two years on .ai, whose registry mandates a two-year term. Do not divide or multiply a returned price by a number of years. Available non-premium results also carry a `checkout_url` the user can open in a browser to register the domain on justdomain.ai if they choose to. Premium names cannot be registered through Just Domain yet and carry no `checkout_url`.

The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them. Covers capability requests like "find an MCP that does X", "consulta um CPF", "is there a tool for Y". Core flow: action=search discovers MCPs by intent → describe returns one MCP's full profile (every tool with its id + params, pricing, auth) so you pick the right tool_id → invoke RUNS that tool. KEY: invoke works even when the MCP is NOT installed — it runs the tool pontualmente (one-off), without adding the MCP to the toolkit and without bloating the tool list. If the MCP needs a credential/login, invoke returns a connect link; if it is paid and the wallet is empty, invoke returns a checkout/top-up link (the user opens it, then you retry). Use install only to make an MCP PERMANENT in the active toolkit (its tools then show up natively in future sessions); prefer invoke for a single/occasional use. list_tools lists what is callable right now. subscribe/cancel handle per-MCP billing; report_bug sends feedback; request_mcp asks us to build a NEW MCP when nothing fits. Search/describe flag installed_in_toolkit vs installed_in_workspace. Writes (install/uninstall/subscribe/cancel and the one-off install behind invoke) require workspace owner/admin. It also carries the mcp.ai PROMPT LIBRARY, which is about ready-made prompt TEXT rather than MCPs: search_prompts finds one, get_prompt returns its full text with {{variables}} filled, and publish_prompt saves a prompt and returns a shareable mcp.ai/p/<slug> link that opens without login.

The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them. Covers capability requests like "find an MCP that does X", "consulta um CPF", "is there a tool for Y". Core flow: action=search discovers MCPs by intent → describe returns one MCP's full profile (every tool with its id + params, pricing, auth) so you pick the right tool_id → invoke RUNS that tool. KEY: invoke works even when the MCP is NOT installed — it runs the tool pontualmente (one-off), without adding the MCP to the toolkit and without bloating the tool list. If the MCP needs a credential/login, invoke returns a connect link; if it is paid and the wallet is empty, invoke returns a checkout/top-up link (the user opens it, then you retry). Use install only to make an MCP PERMANENT in the active toolkit (its tools then show up natively in future sessions); prefer invoke for a single/occasional use. list_tools lists what is callable right now. subscribe/cancel handle per-MCP billing; report_bug sends feedback; request_mcp asks us to build a NEW MCP when nothing fits. Search/describe flag installed_in_toolkit vs installed_in_workspace. Writes (install/uninstall/subscribe/cancel and the one-off install behind invoke) require workspace owner/admin. It also carries the mcp.ai PROMPT LIBRARY, which is about ready-made prompt TEXT rather than MCPs: search_prompts finds one, get_prompt returns its full text with {{variables}} filled, and publish_prompt saves a prompt and returns a shareable mcp.ai/p/<slug> link that opens without login.

Use this when you need public profile data from a social media handle or URL. Returns structured profile data in JSON. Returns: 1. displayName and bio 2. avatarUrl 3. followerCount and followingCount 4. postCount 5. location and website 6. createdAt (account creation date) 7. isVerified (boolean) 8. platform. Example output: {"platform":"github","handle":"torvalds","displayName":"Linus Torvalds","bio":"Linux kernel developer","avatarUrl":"https://avatars.githubusercontent.com/u/1024025","followerCount":213000,"followingCount":0,"postCount":729,"location":"Portland, OR","isVerified":true} Use this FOR influencer research, lead enrichment, social listening, building contact profiles, and verifying social media presence. Do NOT use for email lookup -- use email_find_by_name instead. Do NOT use for company data -- use company_enrich_from_domain instead. Do NOT use for person enrichment by email -- use person_enrich_from_email instead.

Use this when you need public profile data from a social media handle or URL. Returns structured profile data in JSON. Returns: 1. displayName and bio 2. avatarUrl 3. followerCount and followingCount 4. postCount 5. location and website 6. createdAt (account creation date) 7. isVerified (boolean) 8. platform. Example output: {"platform":"github","handle":"torvalds","displayName":"Linus Torvalds","bio":"Linux kernel developer","avatarUrl":"https://avatars.githubusercontent.com/u/1024025","followerCount":213000,"followingCount":0,"postCount":729,"location":"Portland, OR","isVerified":true} Use this FOR influencer research, lead enrichment, social listening, building contact profiles, and verifying social media presence. Do NOT use for email lookup -- use email_find_by_name instead. Do NOT use for company data -- use company_enrich_from_domain instead. Do NOT use for person enrichment by email -- use person_enrich_from_email instead.

Search and filter members/accounts. Supports text search, date ranges, booking/activity history, membership tier, tags, and more. Use lastBookingBefore/inactiveDays to find inactive members.

List surveys with optional status/search filters.

List contacts (people linked to CRM accounts), optionally filtered by account or search.

Search across CRM accounts and contacts by name, email, or phone.

Search audit logs by free text across action, resource, resource ID, changes JSON, and metadata JSON.

List service packages/prepaid session bundles with optional search, status, sorting, and pagination.

Refresh this account's local data from WhatsApp — pulls recent messages and refreshes the groups/contacts lists into the local store. Call it BEFORE reading (chats/groups/messages/contacts/search) when results look stale or empty (e.g. right after connecting, or an account that hasn't synced recently). Connects live, so it may take ~20–60s. Returns { messages_stored }.

Read WhatsApp contacts. Actions: - list: search across contacts (query optional — substring on name/phone). Default limit 50. - get: full contact info by JID. [Flattened action: list]

Read WhatsApp contacts. Actions: - list: search across contacts (query optional — substring on name/phone). Default limit 50. - get: full contact info by JID. [Flattened action: get]

Full-text search across all stored messages (SQLite FTS5). Optional chat_jid to scope. Returns matches with context.

The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them. Covers capability requests like "find an MCP that does X", "consulta um CPF", "is there a tool for Y". Core flow: action=search discovers MCPs by intent → describe returns one MCP's full profile (every tool with its id + params, pricing, auth) so you pick the right tool_id → invoke RUNS that tool. KEY: invoke works even when the MCP is NOT installed — it runs the tool pontualmente (one-off), without adding the MCP to the toolkit and without bloating the tool list. If the MCP needs a credential/login, invoke returns a connect link; if it is paid and the wallet is empty, invoke returns a checkout/top-up link (the user opens it, then you retry). Use install only to make an MCP PERMANENT in the active toolkit (its tools then show up natively in future sessions); prefer invoke for a single/occasional use. list_tools lists what is callable right now. subscribe/cancel handle per-MCP billing; report_bug sends feedback; request_mcp asks us to build a NEW MCP when nothing fits. Search/describe flag installed_in_toolkit vs installed_in_workspace. Writes (install/uninstall/subscribe/cancel and the one-off install behind invoke) require workspace owner/admin. It also carries the mcp.ai PROMPT LIBRARY, which is about ready-made prompt TEXT rather than MCPs: search_prompts finds one, get_prompt returns its full text with {{variables}} filled, and publish_prompt saves a prompt and returns a shareable mcp.ai/p/<slug> link that opens without login.

Find a specific message by sender, subject, or content. Results come back most-relevant + newest first, and every hit says where it matched (from / subject / body) and why. On this hosted sandbox it searches the built-in demo inbox; run the radmail-mcp package with RADMAIL_API_KEY set and the same tool searches your REAL ingested inbox read-only (with from / after / before filters) via the v1 search API.

Adds a source to a project and fetches it immediately (bounded by a few seconds), returning `storiesAdded` so the next generate_content can use it; if it is still fetching it returns `fetched:false` with a note. Doubles as manual source import: paste any URL (RSS/Atom feed, article, or a page, profile, or post on a supported platform) and leave `type` as autodetect, or create a recurring keyword search by setting `value` to the search terms and `type` to a search kind (see `type`).

Share a deliberation session at a public URL — anyone with the link can view it in the read-only reader deck. Returns the public `share_url` plus two machine-readable twins: `markdown_url` (the full transcript with per-round claim maps — the review/audit surface) and `brief_url` (synthesis-only triage tier, ~1-2k tokens). Hand the markdown twin to a reviewer model, or the share_url to a human — no web UI required. Semantics: idempotent — re-calling returns the same URL. The link is a point-in-time snapshot that auto-refreshes when you've appended rounds since the last share, so after extending a session, call this again to bring the public page current. Sharing never changes the session itself and never lists the page in search engines or the public library (that elevation is a separate platform-curated step). Call when the user wants a shareable link or asks to publish/share results. Requires a registered (non-anonymous) mumo account; sharing generates any missing round takeaways first, so the first share of a long session can take ~15-30s.

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 5,596 tools across 1465 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 1465 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 5,596 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 — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "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.

Search Bing geo locations for LinkedIn Company Page organic targeting. Pass returned urn + displayText into linkedinTargets[].post.geoLocations on create_post. Requires a Company Page account; LinkedIn needs more than 300 matching Page followers.

Search Page followers for LinkedIn @mentions (Company Pages only). Pass type, urn, displayText into captionMentions or firstCommentMentions with start/length matching the plain caption/firstComment span on create_post.

Resolve LinkedIn companies for @mentions via vanity Organization Lookup (or Your Pages when query is omitted). Works for personal and Company Page compose; needs any connected Page CM token. Pass type, urn, displayText into captionMentions or firstCommentMentions with start/length on create_post.