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601,754 tools. Updated 2026-09-23 05:53

"A tool for comparing design documents and source code, and accumulating business knowledge" matching MCP tools:

  • Read the FULL TEXT of filings and announcements — with proof links and a knowledge cutoff. ★ NOT `search_filings`. That one ranks passages by similarity and hands you fragments; this hands you whole documents so you can read what was actually said and where it sat in the filing. Similarity is not importance, and a fragment cannot show you its own context. ★ POINT-IN-TIME: pass `as_of` (YYYY-MM-DD). The cutoff is applied in SQL on the source's declared knowledge-time column BEFORE the row limit, so a bounded read is a true prefix of what was knowable, not a random subset of it. **Without `as_of` the read is NOT point-in-time** and says so in `warnings`. ★ NO SENTIMENT, NO SCORES — deliberately. Judging the text is your job. A stored score is one model's output on one day; after that model changes, the stale number still sits in the table looking exactly like a fresh one. ★ READ `corpus_reality` BEFORE CONCLUDING ANYTHING. The full-text corpus is SMALL and the response says how small. One source carries ~1M rows of TITLES ONLY — a large row count there is breadth, not depth, and "what did they say about it" is not answerable from titles. ★ A ticker that returns nothing appears in `coverage.missing`. That means nothing is held for it under those filters — NOT that the company disclosed nothing. Do not fill the gap. Args: source: which corpus, e.g. 'announcements_fulltext' or 'mops_major_event'. tickers: restrict to these codes, e.g. ['2330']. as_of: knowledge cutoff (YYYY-MM-DD). since: optional lower bound on the same knowledge-time column. limit: max documents (these are whole documents; keep it small).
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  • 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.
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  • Show every attempt made for one invoice and what came back. This is the tool that answers "why was it rejected". consult_invoice gives the status; this gives the tax authority's own code, its message, and the request and response exactly as they went over the wire. Read-only. It takes the invoice_id, like reissue_invoice — a rejected document has no access key to look it up by. Get the id from list_invoices. The rows are attempts, not documents: a reissued invoice has more than one, oldest first, and only the last describes the current state. Quote the authority's message rather than paraphrasing it; the code is what the user will search for.
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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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  • LEGACY-ONLY — DO NOT CALL WHEN create-design IS AVAILABLE. If create-design is in the tool list, you MUST NOT call this tool. Call create-design instead. All instructions below apply only when create-design is absent from the tool list. A failed create-design call does not make it unavailable. Generate professionally designed content in Canva including visual designs (posters, social media posts, presentations, flyers) and text-based documents (memos, articles, newsletters, proposals, reports, business plans, requirements documents). Each extra slide adds significant latency (e.g. 15 slides can take 3x longer than 5). Keep to 1-5 slides unless the user explicitly requests more. Use this tool when the user asks you to write, create, generate, or draft ANY document or visual design. Examples: - "Write a memo..." → use this tool to create a Canva Doc - "Generate a business proposal..." → use this tool to create a Canva Doc - "Draft a product overview..." → use this tool to create a Canva Doc DO NOT use this tool for fixed-format visual designs when prepare-design-generation is available. Do NOT use this tool when the user just wants advice, explanations, or information. DO NOT use this tool when the user's message contains a URL and their intent is to create a design FROM that URL — use import-design-from-url instead. Use the 'query' parameter to tell AI what you want to create. The tool doesn't have context of previous requests. ALWAYS include details from previous queries for each iteration. The tool provides best results with detailed context. ALWAYS look up the chat history and provide as much context as possible in the 'query' parameter. Ask for more details when the tool returns this error message 'Common queries will not be generated'. The generated designs are design candidates for users to select from. Ask for a preferred design and use 'create-design-from-candidate' tool to add the design to users' account. The IDs in the URLs are not design IDs. Do not use them to get design or design content. When using the 'asset_ids' parameter, assets are inserted in the order provided. For small designs with few image slots, only supply the images the user wants. For multi-page designs like presentations, supply images in the order of the slides. The tool will return a list of generated design candidates, including a candidate ID, preview thumbnail and url. Before editing, exporting, or resizing a generated design, follow these steps: 1. call 'create-design-from-candidate' tool with 'job_id' and 'candidate_id' of the selected design 2. call other tools with 'design_id' in the response For presentations, target 1-5 slides by default (length: "short"). Format the query string with these sections in order (use the headers exactly): 1. **Presentation Brief** Include: * **Title** (working title for the deck) * **Topic / Scope** (1–2 lines; include definitions if terms are uncommon) * **Key Messages** (3–5 crisp takeaways) * **Constraints & Assumptions** (timebox, brand, data limits, languages, etc.) * **Style Guide** (tone, color palette, typography hints, imagery style) 2. **Narrative Arc** A one-paragraph outline of the story flow (e.g., Hook → Problem → Insight → Solution → Proof → Plan → CTA). Keep transitions explicit. 3. **Slide Plan** Provide numbered slides with **EXACT titles** and detailed content. For each slide, include all of the following subsections in this order (use the labels exactly): * **Slide {N} — "{Exact Title}"** * **Goal:** one sentence describing the purpose of the slide. * **Bullets (3–6):** short, parallel phrasing; facts, examples, or specifics (avoid vague verbs). * **Visuals:** explicit recommendation (e.g., "Clustered bar chart of X by Y (2022–2025)", "Swimlane diagram", "2×2 matrix", "Full-bleed photo of <subject>"). * **Data/Inputs:** concrete values, sources, or placeholders to be filled (if unknown, propose realistic ranges or example figures). * **Speaker Notes (2–4 sentences):** narrative details, definitions, and transitions. * **Asset Hint (optional):** reference to an asset by descriptive name or index if assets exist (e.g., "Use Asset #3: 'logo_dark.svg' as corner mark"). * **Transition:** one sentence that logically leads into the next slide. > Ensure the Slide Plan forms a **cohesive story** (each slide's Goal and Transition should support the Narrative Arc). **Quality checklist (the model must self-check before finalizing)** * Titles are unique, concise (≤ 65 characters), and action-or insight-oriented. * Each slide has 3–6 bullets; no paragraph walls; numbers are specific where possible. * Visuals are concrete (chart/diagram names + variables/timeframes); tables are used only when necessary. * Terminology is defined once and used consistently; acronyms expanded on first use. * Transitions form an intelligible narrative; the story arc is obvious from titles alone. * No placeholders like "[TBD]" or "[insert]". If data is unknown, propose realistic figures and label as "example values". * All required headers and subsections are present, in the exact order above.
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Matching MCP Servers

  • A
    license
    C
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    maintenance
    Provides access to the Reuters Business and Financial News API to retrieve articles, trending news, and market data. It enables searching and filtering financial content by date, author, category, and keywords.
    20
    MIT
  • A
    license
    Not graded
    quality
    B
    maintenance
    Enables MCP clients to connect to a privacy-first, self-hostable workout planning and training log, allowing coaching agents to preview and apply program changes while accessing training data through OAuth-protected endpoints.
    AGPL 3.0

Matching MCP Connectors

  • Ask up to 5 questions across every document grouped in a knowledge space (POST /space-context/{space_id}). Same input and output as claix.window_context.ask, but the answer is built by cross-referencing all the documents in the space instead of a single one, so use it to compare, add up, or reconcile data spread over several files. Requires space_id, the same one passed as the optional space_id when extracting. Each question max 400 characters. Returns user_ask, ia_response, and log_id (null if the answer is in none of the documents). If the API-key owner has verif_space, each ia_response item is {value, source} naming document_id and file_name.
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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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  • 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.
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  • Returns the MCP knowledge version: gitSha, indexedAt, componentCount, patternCount, uptimeSeconds. Call this ONCE per session before generating UI code so you know how fresh the design-system data is. Cheap to call. If gitSha is "unknown" or indexedAt is far in the past, surface that to the user before relying on the data.
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  • Returns a synthesized natural-language answer with citations, grounded in the AlgoVault knowledge bundle (every MCP tool description, response shape, integration tutorial, and code example). Use when you need an explanation, code pattern, or how-to; for raw ranked snippets without LLM synthesis use search_knowledge (faster, no quota cost). Read-only: calls an LLM, no other side effects. Quota: Free 10/month, Starter 50, Pro 200, Enterprise 2000.
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  • Returns ranked snippets from the AlgoVault knowledge bundle answering a question about its MCP tools, response shapes, integration patterns (LangChain, LlamaIndex, MAF, CrewAI), or code examples. Call this BEFORE other tool calls to confirm parameter usage and avoid hallucinating tool shapes. Fast: BM25 lexical search, no LLM call, no quota cost. For a synthesized natural-language answer use chat_knowledge. Read-only, no side effects.
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  • Returns the MCP knowledge version: gitSha, indexedAt, componentCount, patternCount, uptimeSeconds. Call this ONCE per session before generating UI code so you know how fresh the design-system data is. Cheap to call. If gitSha is "unknown" or indexedAt is far in the past, surface that to the user before relying on the data.
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  • List merchant knowledge base documents (uploads + scraped URLs). Use reviewStatus/syncable to see what is ready for agent retrieval. Pass `updatedAfter` for delta sync. Reviewed content is fetched via GET /v6/merchant/ai/knowledge/{id}/content; source audit text is available with ?variant=extracted.
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  • List merchant knowledge base documents (uploads + scraped URLs). Use reviewStatus/syncable to see what is ready for agent retrieval. Pass `updatedAfter` for delta sync. Reviewed content is fetched via GET /v6/merchant/ai/knowledge/{id}/content; source audit text is available with ?variant=extracted.
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  • Find businesses, merchants and websites in the tunnel knowledge base by name or topic. Start here: every other tool needs a `slug`, and this is where a `slug` comes from. Returns an array of summaries, each with `slug`, `kind`, name, description and a `verification` object. Read `verification.level` rather than assuming: "human" means a tunnel employee checked the business, "automated" means machines proved only that the business controls its own channels, and null means neither. Zero matches is a normal answer, not an error — it comes back with `completeness` "empty". Authentication: none. This tool works with no credentials.
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  • List production-type (psr_type) codes. Pass zone= to scope the answer. Most codes are ENTSO-E's B01..B25 and mean the same thing in every ENTSO-E zone. A few are source-native (source != "entsoe") and exist only where that source publishes — they express concepts the B-codes cannot, so they are NOT interchangeable with a similar-looking B-code. Check `source` and read `description` before comparing a code across zones. Passing zone= also returns `taxonomy_note` for zones that mix taxonomies (e.g. GB), and per-code `endpoints` showing where each code comes from. Each code carries `counts_as_generation`: when False the figure is a net flow or net storage number, not production — do not sum it into a generation total.
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  • Permanently delete a link and its click history. Every QR code and shared copy pointing at it stops working immediately, and the short code is released. There is no undo. Prefer update_link when the goal is to send the link somewhere else. Requires a ShareCut API key (Business plan).
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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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  • Read one public SOURCE or SOURCE_GROUP by exact opaque sourceId to inspect provenance, freshness and limitations. Use search_vehicle_knowledge to discover sources; get_model_context or get_answer_context retrieves the knowledge a source supports. Not a search by URL or source label. Never split composite-looking IDs. Unknown ID returns SOURCE_NOT_FOUND. Public read-only access; no knowledge writes. Calls consume rate limits.
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  • THE PRIMARY STANDOUT WORKFLOW. Use this when a user wants an AI-built website made client-ready without choosing individual design tools. First call: pass url (best), source, or business. Standout captures the baseline, scores the real phone and desktop render, chooses a coherent direction, and returns the exact implementation contract for the coding agent. The agent MUST apply the fixes in the repository. Final call: pass the original url/source plus after_url/after_source. Standout compares both versions and produces a client-safe before-and-after report and share link. Do not stop after returning advice; the completed outcome is fix, rerender, prove.
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