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681,027 tools. Updated 2026-10-20 11:57

"Data science and deep learning papers" 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.
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  • Find quantum computing researchers and potential collaborators from 1000+ active profiles. Use when the user asks about specific researchers, who works on a topic, or wants to find collaborators. NOT for jobs (use searchJobs) or papers (use searchPapers). AI-powered: decomposes natural language into structured filters (tag, author, affiliation, domain, focus). Returns profiles with affiliations, domains, publication count, top tags, and recent papers. Data from arXiv papers published in the last 12 months. Max 50 results. Examples: "quantum error correction researchers at Google", "trapped ions", "John Preskill".
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  • Search the PUBLIC Awareness knowledge graph — 1,000+ sourced scientific papers. Use this when the user (especially a researcher or a research agent) asks for peer-reviewed findings: "latest evidence on X", "highly-cited papers about Y", "what does the literature say about Z". This is NOT a memory tool — it does not read the user's private memory, and it needs no per-user API key, so it works even when Awareness memory is not configured (the MCP connection itself still needs its own token). Coverage: condensed-matter, quantum-information, materials, machine-learning, renewable-energy, chemistry, biology, medicine, neuroscience, computer-science (2022-2026, cited >= 50), plus a business supply/demand domain. What comes back (anonymous preview mode): title, snippet (preview), confidence, source_ref (DOI), citation_count, domain/topic, and a detail page URL. Full content is NOT in the preview — tell the user it is paid retrieval via POST /api/v1/public/retrieve with an API key (see https://awareness.market/en/knowledge for the KaaS product). Args: query: Natural-language research question or keywords (2-1000 chars). domain: "science" (default) or "business". topic: Optional sub-topic slug, e.g. "medicine", "condensed-matter", "supply-chain". Empty = search all topics in the domain. top_k: Max results, 1-20 (default 5; anonymous callers are capped at 3). include_graph: When true, also return same-topic related items. Returns: {"results": [{id, title, snippet, confidence, source_ref, citation_count, domain, topic, url}], "related": [...], "total": int, "mode": str} `mode` is "preview" (anonymous) or "full" (authenticated).
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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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  • Searches the Banco Central do Brasil time series (SGS: interest rates, inflation, exchange rates, credit, fiscal and external sector — the curated catalog plus the open data portal index) 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 `bcb_*` 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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  • 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.
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Matching MCP Servers

  • A
    license
    Not graded
    quality
    D
    maintenance
    Enables time-series analysis and forecasting through a structured tool catalogue, including data loading, quality repair, diagnostics, and forecasting with ARIMA, exponential smoothing, Chronos-2, Toto 2.0, and AutoML.
    1
    MIT

Matching MCP Connectors

  • Curated APIs that are SAFE TO EXPERIMENT WITH — no key (or an instant, free, self-serve key), no cost, and no real-world side effects. Free, and deliberately so: this is where you start if you are new here. Every entry carries an `example_request` URL that returned HTTP 200 to an unauthenticated GET on the date shown, so it can be called immediately with no setup. Use this when you are new to this catalog, learning what an API call feels like, testing a tool-use loop, or need a harmless endpoint to demonstrate something. Defaults to the `starter` set; pass grouping="deep-archive" for institutional/OAI-PMH repositories.
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  • START HERE for any open-ended request. Lists the task playbooks this server supports — systematic learning from bookmarks, organising into themes, cleaning up, X-list intelligence, exporting data out, finding a half-remembered save, digests, and diagnosing sync. Each names when to use it; call get_skill for the exact tool sequence.
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  • Free first-level search, limited per day across all callers: current news coverage (100+ languages, 250K+ sources) or scholarly papers (arXiv). Returns structured results immediately with no payment. When the daily quota is exhausted, or when you need live browsing and synthesis across sources, use a9n9_research_quote for paid deep research.
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  • Free first-level search, limited per day across all callers: current news coverage (100+ languages, 250K+ sources) or scholarly papers (arXiv). Returns structured results immediately with no payment. When the daily quota is exhausted, or when you need live browsing and synthesis across sources, use a9n9_research_quote for paid deep research.
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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.
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  • 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 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. Only ids returned by `search` are valid; an unknown id returns an error. The `uis_*` 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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  • UK exam calendar dates verified against JCQ and the exam boards' own documents: results days (A-level 12 August 2027, GCSE 19 August 2027, November 2026 GCSE resits 13 January 2027, Scotland 10 August 2027), the summer 2027 exam timetable (JCQ common window 10 May – 22 June, contingency day 23 June 2027; per board — AQA, Pearson Edexcel, Cambridge OCR and WJEC/Eduqas, all final — the dates of GCSE Maths papers 1–3, English Language papers 1–2, Combined Science papers and A-level Maths papers), entry deadlines (JCQ 21 February 2027) with each board's late and very-late entry fee dates and multipliers, and announced specification changes (first-teaching and first-exam years, DfE reform timetable, Ofqual decisions). Input kind (results, timetable, deadlines, spec-changes; omitted → the listing), optional board (jcq, aqa, pearson/edexcel, ocr, wjec/eduqas, sqa, ofqual, dfe) and qualification (gcse, a-level). Every figure carries its source document, the quoted timetable row, verifiedDate (2026-10-04) and, where the board's 2026 document is still served, the summer-2026 date as previous. An unknown kind, board or qualification answers covered:false with the nearest keys.
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  • Investor-grade market research brief on any market: deep multi-source web research plus market-size searches, key players, news, recent research papers, prediction markets and US grant activity. A model writes market size (with sources and ranges), segments, players, business models, drivers, trends, risks and entry opportunities, an attractiveness score and a PDF. 60-150 s; async=true recommended. If too few sources answer you are not charged (with async=true the core source is checked before the job is queued and charged). Costs $5 USDC per call (x402).
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  • Checks the table one succeeded run sealed against its own manifest. Without deep it reads the manifest, checks it hashes to the digest it was sealed with and names this run's table version, and checks every part the run sealed is among the parts the run lists; no part is fetched. With deep: true it also re-hashes every part this run sealed against the digest the manifest states. Example: {"run_id": "…", "deep": true}. deep reads at most 32 parts and 2147483648 bytes inside one call; a larger run is checked shallowly and says so, and `mr-data verify RUN --deep` from a terminal checks the rest. Parts earlier runs sealed are those runs' to verify. Returns {run_id, table_id, table_version_id, status, layout_format_version, manifest_digest, part_count, row_count, total_bytes, parts_read, deep, findings, parts_unchecked?}. status is table_verified (everything asked was checked and agrees), table_disagrees (findings says what) or table_partially_verified (nothing disagrees, but the deep check was skipped or some parts could not be read: say so, never report it as verified). Read-only, and it charges nothing: no byte leaves in the answer.
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  • Count tweets containing a specific phrase within a date range. Returns the total count of matching tweets (int) directly, or zero if none found. QUERY SYNTAX: Plain keywords (bitcoin, climate change), quoted phrases ("deep learning"), boolean expressions (AI AND crypto, bitcoin OR ethereum, politics NOT sports), or parenthesized groups ((startup OR entrepreneur) NOT "venture capital"). AND/OR/NOT must have a term on both sides. @handles like @karpathy are supported. Field operators (from:, lang:) are stripped. Forward slashes are treated as spaces (24/7 becomes 24 7). Filters: date range (startDate/endDate in YYYY-MM-DD). IMPORTANT!!!!!: THE CURRENT YEAR IS 2026. When user requests relative dates (last week, last month), verify the current date from your system context and double-check the calculated dates - models often get the year wrong, searching one year earlier than intended. Default: startDate=6 months ago if not provided. Use for analytics and trend analysis without retrieving full tweet data. This is a safe, read-only tool for analyzing searchable information.
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  • Coverage map: per-state, per-dataset completeness (officials by level, positions, finance, state-legislative votes). Shows WHERE the data is deep (federal — complete) vs sparse (local). Use it to know whether silence means "no data captured" rather than "nothing there".
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