"Requesting an answer from a specific document" matching MCP tools:

• verify_receiptA

  TunnelMind Data APIB

  Tamper-detection verification for TunnelMind surveillance receipts. Submit the receipt ID, the SHA-256 content hash, and the Ed25519 signature from the receipt document. The registry compares these against what was recorded at issuance time. Returns VALID if both match exactly, INVALID with a specific mismatch reason otherwise. Use this tool when: - You received a surveillance receipt document and want to verify it hasn't been altered. - You are programmatically checking receipt authenticity in an agent workflow. - You want to prove to a third party that a receipt is genuine. Do NOT use this tool when: - You only want to check existence — use `get_receipt` instead (no body required). Inputs: - `receipt_id` (body, required): The receipt's ID field from the document. - `content_hash` (body, required): SHA-256 hex hash of the receipt JSON. Max 256 chars. - `signature` (body, required): Ed25519 signature from the receipt document. Max 512 chars. Returns: - `valid`: boolean. True only if both hash and signature match exactly. - `status`: `VALID` or `INVALID`. - `message`: human-readable explanation. On INVALID, specifies whether the hash mismatched, the signature mismatched, or both. Cost: - Free. No API key required. Latency: - Typical: <100ms, p99: <300ms.

  ConnectorNo auth

• url.qaA

  DocImprintA

  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]."

  ConnectorNo auth

• document.check_claimsA

  apiA

  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%?"

  ConnectorNo auth

• document.summarizeA

  apiA

  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."

  ConnectorNo auth

• approve_requestA

  agentcardA

  Resolve a pending approval request (approve or deny) once the USER has decided. Use this after get_card_details or create_card returns a 202 requiring approval, or for a row from list_pending_approvals. For card_details and transaction, approval automatically completes the follow-up action and returns the result. For cross_app actions (asks from another app: close/pause/resume a card, change a limit, view details), approval records the user's consent and the REQUESTING app completes the action from its side when it retries with the approval id.

  ConnectorNo auth

• fetchA

  FirmTape - SPX dealer positioningA

  Retrieve the full text of one FirmTape document by the id `search` returned: `session:YYYY-MM-DD` for a finished trading session, `page:/path` for an explainer or research page. A FirmTape URL or a bare YYYY-MM-DD trading day is accepted too. Use when: you hold an id from `search`, or a client that only speaks search/fetch (ChatGPT). Not for: structured numbers — get_session and get_levels answer the same day with fields instead of prose. Limits: public FirmTape documents only; long pages are truncated with a link to the rest.

  ConnectorNo auth

• verify_businessA

  BizVerifyA

  Confirm a specific, named business in one jurisdiction — the PRIMARY tool whenever the user wants to verify, check, confirm, or look up a company's existence, status, good standing, or details (e.g. "verify Acme LLC in Delaware", "is Acme registered in FL?", "I need to verify a company in Delaware"). If the user has verification intent but has not given the exact company name, ASK them for the name and use THIS tool — do NOT fall back to search_entities. Two tiers: quick (1 credit) returns existence + status + good-standing. Deep (15 credits, or 25 with force_refresh) adds entity type, formation date, registered agent, officers, principal address, and filing history. Deep is available in a subset of jurisdictions; requesting deep where unavailable returns a quick result with a reason. Requires authentication. A completed verification deducts credits whether or not the business is found — a confirmed no-match is a result. Calls that cannot produce an answer (source unavailable or timed out) are refunded.

  ConnectorNo auth

• get_receiptA

  TunnelMind Data APIB

  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.

  ConnectorNo auth

• parse_pdf_to_textA

  Nordic Financial MCPA

  Download a PDF from a URL and extract all text content, page by page. Use this to read the full text of a specific document — for example, an annual report PDF linked from a search_filings result. Best combined with search_filings: use search_filings to locate the document, then parse_pdf_to_text for the full text. Do not use for PDFs that are already well-represented in the database — search_filings is faster and returns pre-ranked, relevant excerpts. Not suitable for scanned (image-only) PDFs without embedded text; those pages will be returned as "(no extractable text)". Args: pdf_url: Direct HTTPS URL to the PDF file, e.g. https://example.com/report.pdf. Must be publicly accessible; authentication-protected URLs will fail. Returns: All text from the PDF with "--- Page N ---" separators between pages. Returns an error string if the download fails, the URL does not point to a valid PDF, or the document exceeds the 60-second download timeout.

  ConnectorNo auth

• document.summarizeA

  DocImprintA

  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."

  ConnectorNo auth

• collection.askA

  DocImprintA

  Answer a question using RAG over a document collection. Retrieves relevant chunks then synthesizes a cited answer with source attribution. Use when you need a direct answer grounded in your collection documents. For raw matching chunks (without synthesis), use collection.search instead. For single-document Q&A, use url.qa instead. PREREQUISITE: Collection must be populated via collection.add_document and indexed before results appear. Returns: { answer: string, sources: [{ bundle_id, chunk_id }], retrieval: [{ bundle_id, chunk_id, text, score }] } Example prompts: - "What are the key terms of the service agreement in my collection?" - "Based on my due diligence docs, what are the main risks?" - "Answer this question using all documents in the Q4 Contracts collection."

  ConnectorNo auth

• get_latest_filingsA

  AgentFilingsA

  Latest SEC filings for a US public company, as structured JSON: form type, filing date, period, accession number, and a direct link to the document. Pre-indexed, so this is one fast call instead of crawling EDGAR and parsing its index pages. Use it to answer 'what has this company filed recently?' or to locate a specific 10-K/10-Q/8-K before reading it. Price: $0.01 per call (x402 USDC on Base, or a Stripe API key). Check coverage first with probe_coverage (free).

  ConnectorNo auth

• patch_canvas_sectionA

  AgentPlaybooksA

  Edit a specific section of a canvas document. Parallel-safe: only the targeted section is updated. Requires canvas:write or full permission. Lock the section first in multi-agent scenarios. Use write_canvas only when replacing the whole document, and get_canvas_toc to obtain section_id. Pass playbook_id as the UUID or GUID of the playbook this call should target.

  ConnectorNo auth

• llmA

  VaayaA

  Ask a DIFFERENT LLM a question and get its answer, billed per token from the Vaaya wallet (model cost + 3%, usually a fraction of a cent). Use it to get a second opinion from a rival model, cross-check an answer, summarize a huge blob cheaply, or query a specific model the user names (Kimi, GPT, Gemini, Claude, DeepSeek, and 300+ more). `model` accepts 'auto' (default: short prompts go cheap, long go mid), 'cheap' | 'mid' | 'best' tiers, or any exact OpenRouter slug like 'moonshotai/kimi-k3'. Typical costs: cheap tier well under 0.1 cents, best tier 1-3 cents per call. Not for the conversation you are already having — it is a one-shot ask to another model.

  ConnectorNo auth

• describe_routingA

  Registry Expansion RoutingA

  Return the registry routing map: for each entity kind, the set of public registries the expansion agent would consult. Pure, local, no input, no PII. Use it to understand coverage before requesting an (authenticated) expansion.

  ConnectorNo auth

• get_evidence_documentA

  Plumbline — a trust layer for LLM answersA

  Show ONE retrieved evidence document behind an answer you already received, addressed by that answer's correlation_id plus a document_id from its evidence_documents references. Returns the full stored document (title, body, metadata, embedding_text) with the retrieval rank and scores the answer recorded; never the raw embedding vector. Only documents the addressed answer actually recorded resolve: there is no fetch-by-id in general and no way to browse the store. Requires the persisted compliance log and the same session that produced the answer. Absent from the no-auth public demo.

  ConnectorNo auth

• sieve_dataroom_addA

  SieveA

  Add a document to a deal's data room. Creates the deal if needed. This is the primary way to get documents into Sieve for screening. Upload a pitch deck, financials, or any document -- then call sieve_screen to analyze everything in the data room. Provide company_name to create a new deal (or find existing), or deal_id to add to an existing deal. Provide exactly one content source: file_path (local file), text (raw text/markdown), or url (fetch from URL). Args: title: Document title (e.g. "Pitch Deck Q1 2026"). company_name: Company name -- creates deal if new, finds existing if not. deal_id: Add to an existing deal (from sieve_deals or previous sieve_dataroom_add). website_url: Company website URL (used when creating a new deal). document_type: Type: 'pitch_deck', 'financials', 'legal', or 'other'. file_path: Path to a local file (PDF, DOCX, XLSX). The tool reads and uploads it. text: Raw text or markdown content (alternative to file). url: URL to fetch document from (alternative to file).

  ConnectorNo auth

• secedgar_get_filingA

  secedgar-mcp-serverA

  Fetch a specific filing's metadata and document content by accession number. Returns the primary document as readable text. Use offset/next_offset for multi-page access to large filings (10-K, S-1 can exceed 1M chars): pass the next_offset from a truncated response to read the next page. Use section to jump directly to a heading (e.g. 'risk factors', 'item 7') without needing an offset.

  ConnectorNo auth

• kb_get_practical_adviceA

  Hermes — Air Quality IntelligenceA

  Generic protective-action guidance for a category of situation (NOT keyed to an individual user's context). For *personalised* advice that takes the user's specific health situation into account (asthma, pregnancy, gas cooker, tube commute, indoor sources), prefer the Clara MCP server's `contextual_advice` tool — it composes Hermes live readings with personal context to give an answer keyed to *this* user, *now*. Use this KB tool only as a fallback or when Clara is not available. Args: situation: One of "high_pollution_day", "commuting", "exercise", "school_run", "indoor_air", "planning_objection", "pregnancy", "child_asthma". Returns practical advice document (markdown).

  ConnectorNo auth

• get_public_obligationsA

  ApierA

  Retrieve the universal obligation set for a Norwegian entity type — every regulatory obligation that applies by virtue of an entity BEING that organisational form, BEFORE per-company Tier-2 data is layered on. Use this to answer 'what does an AS owe?' or 'what are the baseline filings for an Enkeltpersonforetak?' without naming a specific company. Each obligation carries a tier_2_required boolean — true means the rule engine needs commercial data to know whether it applies to a SPECIFIC company, false means it applies unconditionally. Input: { entity_type } from the closed enum AS / ENK / ANS / DA / NUF (no 'OTHER' fallback). Failure modes: VALIDATION_FAILED, SCOPE_INSUFFICIENT (needs read:rulebook), UPSTREAM_TIMEOUT. For per-company evaluation that DOES layer on commercial data, call get_company_obligations; for a specific company's evaluated obligations, use get_company_obligations instead. Docs: https://www.apier.no/docs/guides/norwegian-company-obligations

  ConnectorNo auth

• document.extract_structuredA

  apiA

  Extract typed fields from document text using a caller-defined schema. Uses a quality AI model with retry logic. Use when you need specific data points from a document rather than full text. For invoices with known fields, document.parse_invoice (prebuilt schema) may be simpler. For general summarization, use document.summarize instead. Schema format: { "field_name": "type hint or description" } — e.g. { "contract_date": "ISO date", "party_a": "string", "penalty_usd": "number" }. Returns: { data: { <field>: value }, data_cited: { <field>: { value, confidence: "high"|"medium"|"low", citations: [{ quote, paragraphs[] }] } } } Example prompts: - "Extract the contract date, parties, and penalty amount from this agreement." - "Pull the vendor name, PO number, and total from this document." - "Get me all named fields from this form using my custom schema."

  ConnectorNo auth