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601,361 tools. Updated 2026-09-22 23:07

"A server for deep research using Google search" matching MCP tools:

  • Search the user's files by filename and return matching documents in the deep-research result shape. ALIAS: this is the SAME search as search_files (same data, same permissions) - use it when your client requires the id/title/url search contract (ChatGPT deep research); otherwise prefer search_files for richer file metadata. Each result's id can be passed to fetch (or get_file) to read that document. Read-only; nothing is written, so it is safe to call.
    ConnectorNo auth
  • Searches the ILOSTAT labour statistics (≈1,200 SDMX dataflows: employment, unemployment, wages, working time, informality, SDG labour indicators) catalog and returns up to 10 matching documents as { id, title, url }, ordered by relevance (an empty list means nothing matched). This tool exists for the OpenAI Deep Research contract: ChatGPT deep research, company knowledge and research workflows over the Responses API require exactly the tools `search` and `fetch`. Pass one of the returned ids to `fetch` to read the document. For direct questions and for data (values, series, rankings) prefer the `ilo_*` tools, which return the actual data with provenance — this is a catalog index, not a data query. Query: natural language or keywords, Portuguese or English; accents and case are ignored. Behavior: read-only and idempotent — the catalog comes from the public source and is cached in memory.
    ConnectorNo auth
  • Search quantum computing research papers from arXiv. Use when the user asks about recent research, specific papers, or academic topics in quantum computing. NOT for jobs (use searchJobs) or researcher profiles (use searchCollaborators). Supports natural language queries decomposed via AI into structured filters (topic, tag, author, affiliation, domain). Date range defaults to last 7 days; max lookback 12 months. Returns newest first, max 50 results. Use getPaperDetails for full abstract and analysis of a specific paper. Examples: "trapped ion papers from Google", "QEC review papers this month", "quantum error correction".
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  • Searches the Brazilian Federal Senate open data (senators in office and active committees of the Senate and the National Congress) catalog and returns up to 10 matching documents as { id, title, url }, ordered by relevance (an empty list means nothing matched). This tool exists for the OpenAI Deep Research contract: ChatGPT deep research, company knowledge and research workflows over the Responses API require exactly the tools `search` and `fetch`. Pass one of the returned ids to `fetch` to read the document. For direct questions and for data (values, series, rankings) prefer the `senado_*` tools, which return the actual data with provenance — this is a catalog index, not a data query. Query: natural language or keywords, Portuguese or English; accents and case are ignored. Behavior: read-only and idempotent — the catalog comes from the public source and is cached in memory.
    ConnectorNo auth
  • Searches the UNESCO UIS statistics (≈5,000 indicators: education — enrolment, completion, literacy, teachers, spending, SDG 4 —, science/R&D (SDG 9.5), culture (SDG 11.4) and demographic context) catalog and returns up to 10 matching documents as { id, title, url }, ordered by relevance (an empty list means nothing matched). This tool exists for the OpenAI Deep Research contract: ChatGPT deep research, company knowledge and research workflows over the Responses API require exactly the tools `search` and `fetch`. Pass one of the returned ids to `fetch` to read the document. For direct questions and for data (values, series, rankings) prefer the `uis_*` tools, which return the actual data with provenance — this is a catalog index, not a data query. Query: natural language or keywords, Portuguese or English; accents and case are ignored. Behavior: read-only and idempotent — the catalog comes from the public source and is cached in memory.
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  • Returns the full document for an id obtained from `search`, as { id, title, text, url, metadata }: `text` is the readable content (Markdown) and `url` the canonical public page to cite. Companion of `search` in the OpenAI Deep Research contract, over the ILOSTAT labour statistics (≈1,200 SDMX dataflows: employment, unemployment, wages, working time, informality, SDG labour indicators) catalog. Only ids returned by `search` are valid; an unknown id returns an error. The `ilo_*` tools remain the tools for data queries. Behavior: read-only and idempotent — a live GET against the public source when the document needs it.
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Matching MCP Servers

  • A
    license
    Not graded
    quality
    D
    maintenance
    Enables deep research tasks using a multi-agent architecture that integrates any LLM and MCP tools. Available via MCP stdio, streamable HTTP, and SSE transports.
    17
    MIT
  • A
    license
    A
    quality
    C
    maintenance
    An open-source deep research MCP server that provides multi-source web search and synthesis with citations, enabling agents to perform citation-backed research using Qwen3-30B-A3B-Thinking and other models.
    6
    60
    MIT

Matching MCP Connectors

  • Autonomous deep research reports merging PSFK trend graphs with citable sources.

  • Autonomous buy-side research: diligence, earnings, SEC filings, comp sets. Source-cited real data.

  • Purpose: ChatGPT-connector-standard document fetch by id from `search` results. Namespaces: `tool:{name}` returns the tool's full documentation and how to call it; `resource:{uri}` returns the resource's live data (core resources resolved server-side — also the bridge for clients without MCP resource support, e.g. Gemini); `signal:{market}:{symbol}` returns the symbol's latest combined research signal. Triggers: ChatGPT connectors / Deep Research call this after `search`. Clients without MCP resource support can call it directly with a known resource id, e.g. fetch("resource:market://global/summary"). When to call: whenever the full content behind a search result id is needed. Prerequisites: a valid id — from `search` results or a known namespace id. Next steps: for tool docs, call the named tool via tools/call; for signals, get_signal_detail / explain_decision for deeper evidence. Caveats: uncovered resource uris return description-only text (no fabricated data). `text` is a JSON document for resource/signal ids. Output: {id, title, text, url, metadata, disclaimer, is_investment_advice, data_classification} — flat envelope, OpenAI fixed shape.
    ConnectorNo auth
  • Search the Melvea local honey directory by free-text query and return matching producers as a list of results (id, title, url). Designed for ChatGPT Deep Research and Company Knowledge. Use for any local-honey discovery query that names or implies a place; the tool parses place and varietal from the query. Returns an honest empty list when nothing matches — never fabricate. Pair with fetch to retrieve full producer detail.
    ConnectorNo auth
  • General-purpose Google search — returns organic results for any query. Unlike search_google_xray (LinkedIn-only), this searches the entire web. Useful for finding job postings on portals (jobs.cz, prace.cz, profesia.sk, indeed.com), company info, news, or any other web content. Results are NOT saved to contacts — use this for research and discovery. Capped at 4 calls per minute to protect the Serper/Google budget.
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  • Fetch one document's full extracted text by id (a file id from search / search_files / list_files), in the deep-research result shape. ALIAS: this is the SAME read as get_file (same data, same permissions, same audit, same size guard - large files are truncated) - use it when your client requires the id/title/text/url fetch contract (ChatGPT deep research); otherwise prefer get_file, which also serves download links and inline images. Read-only; audited.
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  • Search Quantustik for S&P 500 tickers by symbol or company name. Paired with fetch — this is the two-tool "search"/"fetch" convention ChatGPT connectors and deep-research clients expect from an MCP server: call search first to get lightweight hits, then fetch(id) on the one(s) worth reading in full. Args: query: Ticker symbol (e.g. "NVDA") or company-name substring (e.g. "nvidia", "apple"). Case-insensitive. Returns a dict with a `results` list of up to 10 {id, title, url} objects — id is the ticker symbol, ranked exact-symbol match first, then company-name/ticker prefix, then substring. Empty query or no scan data returns an empty list, never an error.
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  • Research keyword demand: search volume, difficulty, CPC, and intent, plus keyword ideas, Google Trends interest and related queries, and Google Ads keyword performance. Volume and difficulty come from a live keyword vendor: a workspace without vendor credits gets an explanatory error with the path forward, never fabricated numbers. Use for what to target and how much demand exists; for who ranks today use serp_competitors. Already scoped to the connected workspace and its site; call directly, no domain or site parameter is needed. Cost: metrics and suggestions bill AI credits (live vendor data at actual cost); trends, related_queries, compare and performance are FREE (Google Trends / your connected Google Ads).
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  • Searches the IBGE (Brazilian official statistics: SIDRA tables, municipalities, known indicators) catalog and returns up to 10 matching documents as { id, title, url }, ordered by relevance (an empty list means nothing matched). This tool exists for the OpenAI Deep Research contract: ChatGPT deep research, company knowledge and research workflows over the Responses API require exactly the tools `search` and `fetch`. Pass one of the returned ids to `fetch` to read the document. For direct questions and for data (values, series, rankings) prefer the `ibge_*` tools (`ibge_sidra`, `ibge_cidades`, `ibge_indicadores`, `ibge_comparar`…), which return the actual data with provenance — this is a catalog index, not a data query. Query: natural language or keywords, Portuguese or English; accents and case are ignored. Behavior: read-only and idempotent — the catalog comes from the public source and is cached in memory.
    ConnectorNo auth
  • Searches the medical terminologies (CID-10 categories and chapters, ICD-11, LOINC, RxNorm, MeSH, terminology version records) catalog and returns up to 10 matching documents as { id, title, url }, ordered by relevance (an empty list means nothing matched). This tool exists for the OpenAI Deep Research contract: ChatGPT deep research, company knowledge and research workflows over the Responses API require exactly the tools `search` and `fetch`. Pass one of the returned ids to `fetch` to read the document. For direct questions and for data (values, series, rankings) prefer the terminology tools (`icd11_*`, `cid10_*`, `loinc_*`, `rxnorm_*`, `mesh_*`, `atc_*`, `map_*`, `find_equivalent`, `validate_codes`), which return the actual data with provenance — this is a catalog index, not a data query. Query: natural language or keywords, Portuguese or English; accents and case are ignored. Behavior: read-only and idempotent — the catalog comes from the public source and is cached in memory.
    ConnectorNo auth
  • Add an extra clickable shortcut (sitelink) that appears beneath a Google Ads ad — helpful deep-links like "See Pricing", "Book a Demo", or "Contact Sales" with two short description lines. Sitelinks lift click-through rate by giving searchers alternate landing paths to the same advertiser. ALSO KNOWN AS: sitelink, site link, extra link, ad link, secondary link, deep link, additional link, jump link KEYWORDS: sitelink, site link, link, deep link, shortcut, google ads, extension, pricing link, demo link, contact link, landing page, CTR, click through WHEN TO USE: - "Add a sitelink to our Google Ads library" - "Create a 'See Pricing' / 'Book a Demo' / 'Contact Sales' link extension" - "Register a new sitelink for the homepage redesign" - "I need a shortcut under our search ads that points to the new pricing page" WHEN NOT TO USE: - Want a non-clickable selling-point snippet ("Free Shipping", "24/7 Support") → use create_google_callout_extension - Want a labeled list of offerings (e.g. "Brands: Nest, Nexus") → use create_google_structured_snippet_extension - Same sitelink for Microsoft Ads / Bing → use create_microsoft_sitelink_extension (library entries are per-channel) - Anything for Facebook / LinkedIn / Reddit — ad extensions are a search-channel-only concept INPUTS (all required): - url: destination the sitelink opens. - link_text: visible link text (1-25 chars). - description1: first description line under the link (1-35 chars). - description2: second description line (1-35 chars). EXAMPLE: create_google_sitelink_extension( url="https://example.com/pricing", link_text="See Pricing", description1="Plans for every team size", description2="Start free, upgrade anytime", ) Returns the persisted extension (id, externalId, type=SITELINK, channel=GOOGLE_ADS). A 400 usually means a character-limit was exceeded.
    ConnectorAPI key
  • Search across your own connected-account content and return the best matches. Each result has an `id` (pass it to `fetch` for the full item), a `title`, a `url`, and a `text` snippet. This is the deep-research "search" entrypoint the ChatGPT/Claude connectors call by convention; for semantic search over analyzed videos specifically use `search_videos`. Returns {"results": [...]}; when you have no connected accounts it returns reason="no_connected_accounts" plus a connect_url instead of results.
    ConnectorOAuth
  • List supported Google Maps place type values for search filters. Returns place_types as a string array. Use a value with place_type on google-maps.search or google-maps.nearby_search. Cost = 1 token.
    ConnectorNo auth
  • Scrape a full Wikipedia page (sections, infobox, references). Heavier than lookup/wikipedia. Use for deep research. Example call: {"page": "Anthropic"} Cost: $0.005–$0.05 USDC on Base per call.
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  • Exact Google Ads search volume for `<keyword>` — Google's own monthly search-volume numbers (plus competition and CPC) from the Ads API, for up to 10 keywords. Use when you specifically need Google Ads figures; for general SEO volume + keyword difficulty, prefer seo_keyword_overview (cheaper). Example: seo_keyword_google_ads_volume({ keywords: ["running shoes"], location_code: 2840, _apiKey: "your-base64-key" })
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  • Canonical code-lookup tool for this server. Search Loa's CPT/HCPCS index using exact codes, clinical terms, or consumer phrases. Use this first when the user does not already know the CPT code, before calling pricing tools.
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  • Multi-step cited research in one call: plans sub-questions, searches each, fetches and dedupes sources, then synthesizes an answer with inline [n] citations, key findings, and gaps. Unlike `research` ($0.08, one search + summary) this decomposes the question and cites every claim. SLOW: a standard run takes ~30-60 seconds — use a generous client timeout and do not retry on timeout. Price: $0.20 standard (3 sub-questions, 8 sources) / $0.35 deep (5 sub-questions, 12 sources)
    ConnectorNo auth