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

Browse the analyses you can run — the ones you commissioned plus the platform Standard Library (prebuilt tools; each result tagged source:'own' or 'standard_library'). Plain-language match; no query lists everything, your own first. Nothing fits? Commission it with create_analysis.

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

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.

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.

Search Telegram: discovers public channels by name/topic (directory lookup) and finds indexed public post/channel pages (semantic web index scoped to t.me). Telegram has no global full-text search, so treat results as discovery: find the right channel here, then use glim_telegram_get(ref=<handle>) for its live posts or glim_telegram_get(ref, query=...) to search within that channel's full history.

Fetch a public Telegram channel or post - no account or API key involved. Channel refs (@handle, handle, t.me URL) return channel info + recent posts newest-first with views, reactions, media, polls, and link previews; pass query to search within the channel's full history, and page older posts with before=<next_cursor>. Post refs (t.me/<channel>/<id>) return that post plus its most recent discussion comments. Media URLs are Telegram CDN links that expire within hours - fetch promptly, never store. Only channels with a public web preview work (most public channels); private channels and groups are not accessible.

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).

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.

Query the current state of any in-flight async operation by operation_id. WHEN TO USE: Use to poll the state of a pending_async operation when no webhook callback has arrived or to check progress. WHEN NOT TO USE: Do not poll more frequently than once per 10 seconds — use webhook delivery for real-time updates instead. COST: free - no key required LATENCY: ~50ms

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.

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]

Show how far back the LOCAL message archive reaches per chat (oldest_ts, newest_ts, message_count, status). Use it to find pagination boundaries for whatsapp_messages and to decide whether to pull more with whatsapp_history_backfill. Optional query (filter by name/JID), kind, limit, or a single chat_jid.

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).

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 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.

Track trending topics across social media with velocity scores — what's gaining attention right now on Reddit, Hacker News, and Google Trends. PAID: $0.01 per query after a daily free allowance (25/day). On a 402, follow the returned payment instructions and re-call with the SAME args plus payment_tx=<reference>. An Authorization: Bearer fnet_ key bypasses it.

Analyze cross-platform sentiment for a topic on Reddit and Hacker News — an overall score + per-platform breakdown, volume trend (rising/steady/new), and key discussion threads. Deterministic lexicon heuristic (no LLM). PAID: $0.01 per query after the daily free allowance (25/day). On a 402, follow the returned payment instructions and re-call with the SAME args plus payment_tx=<reference>. An Authorization: Bearer fnet_ key bypasses it.

Track viral content gaining traction fastest on Reddit and Hacker News — posts and stories ranked by velocity (engagement per hour since posting), the "what's about to blow up?" tool. PAID: $0.01 per query after the daily free allowance (25/day). On a 402, follow the returned payment instructions and re-call with the SAME args plus payment_tx=<reference>. An Authorization: Bearer fnet_ key bypasses it.

Check the pulse of a community on Reddit or Hacker News — its hot topics, aggregate sentiment, and activity level (high/moderate/low by total engagement). Point it at a subreddit, a Hacker News topic, or both. PAID: $0.01 per query after the daily free allowance (25/day). On a 402, follow the returned payment instructions and re-call with the SAME args plus payment_tx=<reference>. An Authorization: Bearer fnet_ key bypasses it.

Track brand mentions across Reddit and Hacker News with sentiment + context — who's talking about a brand or product, where, how they feel, and the threads driving it. Premium. PAID: $0.02 per query after the daily free allowance (25/day). On a 402, follow the returned payment instructions and re-call with the SAME args plus payment_tx=<reference>. An Authorization: Bearer fnet_ key bypasses it.

Search FavCRM MCP tools, enabled skills, and feature areas for how to use the platform. Use before guessing tool names or JSON arguments.

Search this company's reviewed knowledge base and return grounded snippets with source document IDs. Use for merchant policies, FAQs, pricing notes, brand guidance, and other uploaded references.

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.

Verify a single email address — deliverability and quality signals for lead enrichment, signup gating, and list hygiene. Returns deliverable (true/false/unknown), mx_valid, disposable (throwaway/temp-mail), role_account (info@, support@, …), free_provider (gmail, yahoo, …), domain_age_days, and a best-effort smtp_check. Domain-level facts are cached 7 days. PAID: $0.005 USDC per query after a daily free allowance (25/day). On a 402, pay the returned Solana memo and re-call with the SAME args plus payment_tx=<signature>. An Authorization: Bearer fnet_ key bypasses payment.

Use this when you need to audit email deliverability configuration for a domain. Returns structured JSON with authentication record analysis and a deliverability score 0-100. POST variant of email_audit_deliverability -- same params passed as JSON body instead of query string. 1. score (number 0-100) -- overall deliverability health score 2. spf (object) -- SPF record found, valid syntax, includes count, too-many-lookups flag 3. dkim (object) -- DKIM selectors tested (google, default, selector1, selector2), which ones pass 4. dmarc (object) -- DMARC record found, policy (none/quarantine/reject), rua/ruf reporting addresses 5. mx (object) -- MX records found, priorities, mail server hostnames 6. recommendations (array) -- prioritized list of fixes to improve inbox placement Example output: {"score":65,"spf":{"found":true,"valid":true,"record":"v=spf1 include:_spf.google.com ~all"},"dkim":{"google":true,"default":false},"dmarc":{"found":true,"policy":"none","record":"v=DMARC1; p=none"},"mx":[{"priority":10,"exchange":"alt1.gmail-smtp-in.l.google.com"}],"recommendations":["Upgrade DMARC policy from none to quarantine","Add DKIM for default selector"]} Use this BEFORE launching email campaigns, onboarding new domains for outreach, or diagnosing inbox placement issues. Essential for email marketers, sales teams, and IT admins managing domain reputation. Do NOT use for single email validation -- use email_verify_address instead. Do NOT use for finding email addresses -- use email_find_by_name instead. Do NOT use for domain WHOIS/DNS -- use domain_lookup_intelligence instead.

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`.