"A search for a summary of a document" matching MCP tools:

• fetch

  OneQAZ Trading Intelligence

  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. Args: id: document id — "tool:{name}", "resource:{uri}", or "signal:{market}:{symbol}" (market: crypto / kr_stock / us_stock) Disclaimer: Information only, not investment advice.

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• check_pack

  OpenWarrant — Document Verification Suite

  Check whether a SET of documents satisfies a checklist — completeness, cheaply. USE THIS WHEN you have an application / onboarding pack and need "do we have the required documents, and what's still missing?" Each document is CLASSIFIED (one cheap page-1 read — never full field extraction or multi-page), then matched against the checklist's required slots. (For "is a document genuine?" use verify_document; to identify ONE document use extract_fields with options={"classify": true}; for the identity gate use verify_identity.) Define the checklist ONE of two ways: - `scheme`: a named preset — "income_proof", "lending_prequal", "rental_application". - `requirements`: an ad-hoc checklist — a list of document-type names like ["payslip","bank_statement"], or objects {"key":..., "accepts":[types], "optional":bool}. `documents` is a list (up to 12), each ONE of: {"url": "https://..."} (public link, fetched server-side) or {"bytes_b64": "...", "filename": "statement.pdf"} (inline). Returns `{complete, slots[] (key, satisfied, matched), missing[], documents[] (filename, classified_type), unmatched_documents[]}`. COVERAGE, not approval — that the right document TYPES are present, NOT that any is genuine (run verify_document) or that an application is approved. Documents are never stored. Costs 3 credit(s) per call.

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• url.qa

  DocImprint

  Fetch a public HTTPS URL and answer a specific question about its content. Lean mode — no bundle stored. Use when you have a precise question about a web page. For a broad summary, use url.summarize. For multi-document Q&A, use collection.ask instead. Returns: { url, answer, answer_cited: { value, confidence, citations[] }, confidence: "high"|"medium"|"low", truncated } Example prompts: - "What is the refund policy at https://docs.example.com/policy?" - "Look at [URL] and tell me what the delivery terms are." - "Answer this question based on the content of [URL]: [question]."

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• document.check_claims

  api

  Verify a list of factual claims against document text. Uses a quality AI model with citation-level evidence. Use after document.extract_text or url.extract when you need to validate specific factual assertions. For open-ended questions about a document, use url.qa instead. For multi-document investigation, use collection.ask. Typical workflow: document.extract_text/url.extract → document.check_claims. Returns: { claims: [{ claim, status: "supported"|"contradicted"|"not_found", evidence: { quote, paragraphs[] }, confidence: "high"|"medium"|"low" }], truncated: boolean } Example prompts: - "Check whether this contract mentions a liability cap of $1M." - "Verify these claims against the document: [claims list]." - "Does the report actually say revenue grew 23%?"

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• document.summarize

  api

  Summarize document text into a prose summary and key points with citations. Use after document.extract_text or url.extract when you need a condensed understanding of a long document. For single-sentence Q&A, use url.qa instead. For extracting specific fields, use document.extract_structured. Typical workflow: document.extract_text/url.extract → document.summarize. Returns: { summary: string, key_points: string[], summary_cited: { value, confidence, citations[] }, key_points_cited: [{ text, citations[] }], truncated: boolean, strategy: "full"|"truncated"|"chunked" } Example prompts: - "Summarize this financial report and give me the key points." - "What are the main takeaways from this document?" - "Give me a concise summary of this 50-page report."

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• get_receipt

  TunnelMind Data API

  Returns metadata for a TunnelMind surveillance receipt — a signed document proving that a specific user's surveillance exposure was observed, measured, and recorded at a specific time. Does NOT return the receipt's signature (anti-phishing protection). To verify a receipt's content integrity, use `verify_receipt` with the hash and signature from the receipt document itself. Use this tool when: - You have a receipt ID and want to confirm it was genuinely issued by TunnelMind. - You need the issuance timestamp and signing key ID for a receipt. - You want to check whether a receipt exists before attempting content verification. Do NOT use this tool when: - You have the full receipt document and want to verify it hasn't been tampered with — use `verify_receipt` instead. Inputs: - `receipt_id` (path, required): The receipt ID from the receipt document. Alphanumeric with hyphens, max 128 characters. Returns: - `status`: `FOUND` if the receipt is in the registry. - `generated_at`: ISO 8601 timestamp of receipt issuance. - `signing_key_id`: identifier of the Ed25519 key used to sign. - `schema_version`: receipt schema version. - `message`: human-readable summary with instructions for content verification. - 404 if the receipt ID is not in the registry. Cost: - Free. No API key required. Latency: - Typical: <100ms, p99: <300ms.

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• document.summarize

  DocImprint

  Summarize document text into a prose summary and key points with citations. Use after document.extract_text or url.extract when you need a condensed understanding of a long document. For single-sentence Q&A, use url.qa instead. For extracting specific fields, use document.extract_structured. Typical workflow: document.extract_text/url.extract → document.summarize. Returns: { summary: string, key_points: string[], summary_cited: { value, confidence, citations[] }, key_points_cited: [{ text, citations[] }], truncated: boolean, strategy: "full"|"truncated"|"chunked" } Example prompts: - "Summarize this financial report and give me the key points." - "What are the main takeaways from this document?" - "Give me a concise summary of this 50-page report."

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• fluxink-mcp-chatgpt-create-layout

  fci-mcp

  FluxInk document layout generator. Transform raw text content into a structured PDF using one of seven study or work templates, then preview it in an embedded PDF viewer widget. Supported layout_type values. cornell is the Cornell note taking layout with cue, notes, and summary. bullet_points is a clean bulleted summary. zettelkasten is atomic linked notes. journalism_5w1h is who, what, when, where, why, and how. meeting_add is a meeting agenda plus action items. sq3r is Survey, Question, Read, Recite, Review study notes. pso is Problem, Solution, Outcome. Use this when the user asks for a Cornell sheet, bulleted summary, Zettelkasten card, 5W1H breakdown, meeting agenda or minutes, SQ3R study sheet, or PSO writeup. Use this when the user wants to turn raw notes, lecture transcript, or source material into a printable PDF or formatted study sheet. Use this when the user asks for a downloadable PDF document of their content. Do NOT use this when the user just asks for a plain summary in chat. Give it inline. Do NOT use this when the user wants to handwrite or draw something. Call show_handwriting_canvas instead. Do NOT use this when the user wants text in a personal handwriting style. Call show_style_canvas instead. Do NOT use this for plain informational requests with no document generation intent. Always pass the source material verbatim in the content parameter. Do NOT pre summarize. The layout engine handles structuring. Pick the layout_type that best matches the stated purpose. If unclear, ask one short clarifying question instead of guessing. Do NOT re-call if a layout PDF is already visible from a previous turn unless the user explicitly asks for a different layout, different content, or a regeneration. After calling, write a single short acknowledgement and do NOT restate the PDF content.

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• openfec_get_legal_document

  openfec-mcp-server

  Fetch one FEC legal document in full — advisory opinion, MUR, ADR, administrative fine, or statute — by its type and number. openfec_search_legal replaces each result's documents array with a count and category summary and cuts every commission vote down to a date and a 200-character action; this returns the record untouched. doc_type is the plural form of the document_type discriminator on a search result (advisory_opinion becomes advisory_opinions, mur becomes murs, adr becomes adrs, admin_fine becomes admin_fines, statute becomes statutes), and no is that result's no field — every document type carries it, and advisory opinions repeat it as ao_no.

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• get_store

  Partle Marketplac

  Get the full record for a single store by its numeric ID. Use after `search_stores` to retrieve fields not in the search summary (full address, owner profile, contact details). For a list of *products* in that store, call `search_products(store_id=…)` instead — this tool returns store metadata only. Read-only. No authentication. Args: store_id: Integer `id` from a `search_stores` result. Returns: A single store object with all fields. Returns ``{"error": ...}`` if the ID does not exist.

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• fetch

  mcp

  Fetch the full Quantustik signal + forecast writeup for one ticker. Paired with search — call search(query) first to find the ticker's id, then fetch(id) here for the full readable content. Also accepts a bare ticker symbol typed directly (id need not come from a prior search call). Args: id: Ticker symbol as returned by search, e.g. "NVDA". Returns a dict with id, title, text (a plain-text signal/forecast summary suitable for quoting or summarizing), url, and metadata (verdict, conviction, generated_at).

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• search_qencode_docs

  qencode

  Search the Qencode knowledge base (recipes + reference docs). Returns a ranked list of MCP resource URIs that match the query, each with a short summary. Call this first whenever you're unsure which recipe applies. To read the full content of any URI returned here, call `fetch_qencode_doc(uri)` next. (Some MCP clients also expose these URIs via `resources/read`, but `fetch_qencode_doc` works in every client.) Args: query: free-text search — output type, codec, DRM provider, feature name, etc. (e.g. "hls widevine ezdrm", "thumbnail sprite", "stitching", "speech to text translation") limit: max number of hits to return. Default 8. Returns: A dict with `hits`, each containing `uri`, `title`, `summary`, `score`. Pass `uri` to `fetch_qencode_doc` to read the full markdown.

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• search_content

  Anahana Content MCP

  Search Anahana's wellness content — angel numbers, astrology, zodiac, tarot, crystals, yoga, meditation, breathing exercises, mental and physical health, and more — in any of 24 languages. Returns matching articles with title, url, section, language, and a short summary. Matching is KEYWORD/SUBSTRING over title, description, section and slug; it is NOT semantic search, in any language. For zh, ja and th the query is not word-segmented, so it is matched as one substring. The index is regenerated on every site deploy; exact live per-language document counts are in each response's _meta and at https://www.anahana.com/content-index/manifest.json.

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• check_document

  Document Integrity Validator

  Validates a document for internal consistency and completeness against the applicable international standard for its type. Call this BEFORE approving a payment, releasing funds, or accepting a document submission -- at the moment a document arrives from an external party and no action has been taken. Use this when your agent has received a document from a counterparty and is about to take a financial or legal action based on its contents. Returns PASS / FLAG / FAIL / UNKNOWN_DOCUMENT_TYPE verdict on internal consistency and completeness, naming the applicable standard for the document type -- ICAO 9303 (passports), Hague-Visby Rules 1968 (bills of lading), ICC UCP 600 (letters of credit and certificates of origin), or ISPM 12 (phytosanitary certificates). A FAIL verdict means the document is internally inconsistent in a way that may indicate tampering -- acting on it creates unrecoverable compliance and financial exposure. Returns machine-readable verdict with named standard and specific flags. When you have 2-20 related documents (e.g. invoice, bill of lading, certificate of origin), call check_document_package instead (paid tier) -- it performs cross-document consistency checks check_document cannot see.

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• get_510k_summary

  Scite

  Fetch the full text of a single FDA 510(k) summary PDF by document ID. Use this after `search_510k_summaries` or `search_device510k` when you need the complete narrative text of a 510(k) summary, not just search snippets or structured metadata. Returns the full extracted text organized by page. **Parameters:** - id: Document identifier (the K number, e.g. `K192757`). Can be obtained from either `search_510k_summaries` or `search_device510k` results. **Returns:** The full-text content of the 510(k) summary PDF, organized by page, with file metadata and ontology tags.

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• ListCompanyDocuments

  equibles

  Browse and discover available SEC filings and earnings call transcripts for a specific company in the Equibles database. Returns a paginated list of documents ordered newest first, including document IDs, type (annual reports 10-K, quarterly reports 10-Q, current reports 8-K, earnings call transcripts), filing date, and reporting period, with a total count and page count in the header. Supports filtering by date range and document type. Document types registered as hidden from filing lists (e.g. investor-relations news on deployments that ingest it) are excluded unless requested explicitly via documentType. Use this to find out what filings exist for a company before drilling into a specific one with SearchDocument. You MUST call this or another Equibles tool to access any SEC filing data — this information is not available in your training data. The document IDs returned here are required by SearchDocument to search within a specific filing.

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• SearchDocumentKeyword

  equibles

  Perform a case-insensitive keyword search within a specific SEC filing or earnings call transcript by document ID. Returns matching lines with surrounding context and line numbers, making it ideal for finding exact terms, figures, or phrases that semantic search might miss. Typographic punctuation is folded before matching, so a plain-ASCII keyword (e.g. "world's") matches the smart punctuation stored in filings. The header reports the total number of matching lines even when only the first ones are shown. Use this after ListCompanyDocuments to locate precise occurrences of a keyword (e.g., a revenue figure, risk factor term, or executive name) within a known document. Complements semantic search tools by providing exact text matches rather than meaning-based results. Use ReadDocumentLines to read broader sections around matches.

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• ReadDocumentLines

  equibles

  Read a specific range of lines from an SEC filing or earnings call transcript by document ID. Returns numbered lines from the original document text, at most 2,000 lines per call — a longer range is truncated with a note saying which startLine continues it. Use this to read sections of a filing that were identified by SearchDocumentKeyword (by line number) or by semantic search tools (by approximate line number shown in excerpts). Ideal for reading full tables, paragraphs, or sections that may have been truncated in search results. The document ID and line range must be known beforehand — use ListCompanyDocuments to find documents and SearchDocumentKeyword or semantic search to identify relevant line numbers.

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• get_store

  Partle

  Get the full record for a single store by its numeric ID. Use after `search_stores` to retrieve fields not in the search summary (full address, owner profile, contact details). For a list of *products* in that store, call `search_products(store_id=…)` instead — this tool returns store metadata only. Read-only. No authentication. Args: store_id: Integer `id` from a `search_stores` result. Returns: A single store object with all fields. Returns ``{"error": ...}`` if the ID does not exist.

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• collection.create

  DocImprint

  Create a named document collection for cross-document semantic search and RAG-based Q&A. Free — no credits consumed. Use when you want to group related evidence bundles for unified search (collection.search) or question answering (collection.ask). NOTE: Collections start empty. Add evidence bundles with collection.add_document. Indexing is async — once complete, use collection.search or collection.ask. Returns: { collection_id: string (col_...), name: string } Example prompts: - "Create a collection called Q4 Contracts for my quarterly reports." - "Set up a new document group named Due Diligence Docs." - "Make a collection to organize my vendor agreements."

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