"A tool for processing and extracting data from PDFs into structured formats" matching MCP tools:

• 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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• convert_data

  mcp

  Use this when you need to convert tabular data between JSON (array of objects), CSV, TSV, and XML instead of hand-transforming it. Deterministic: same input, same output. Handles quoted CSV fields (embedded commas, escaped "" quotes), flattens nested objects into dotted keys (b.x), and takes the union of keys across all rows so ragged data still lines up in columns. CSV/TSV input needs a header row plus at least one data row; JSON input must be an array of objects. Example: {from:'csv', to:'json'} on "name,age\nAda,36\nGrace,45" -> rowCount 2 and an output JSON array of two objects. Returns the input/output formats, the parsed row count, and the serialized output document as a string.

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• convert_document

  Kamy

  Convert a file you already hold — .docx, .xlsx or .csv — into a PDF, preserving its existing content. Pass the bytes base64-encoded together with the original filename, which is what the API uses to detect the input type. This is the inbound direction: it consumes an existing document, whereas render_docx / render_xlsx / render_pptx GENERATE new documents from structured data, and render_pdf builds one from a template. Returns a stored render { id, url, bytes, durationMs, name } whose id can be fed straight into merge_pdfs, split_pdf, edit_pdf, create_signature_request or create_envelope. Counts one render against the monthly quota. Files over 10 MB are refused by this tool because the bytes travel through the tool call. Requires a Kamy API key with the `render` scope; without a key, returns dashboard setup instructions.

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• law_citations_format_oscola

  UK Business Tools - Ledgerhall

  USE THIS TOOL AFTER citations_resolve to produce the correctly formatted OSCOLA citation string. Pass the parsed fields returned by citations_resolve directly into this tool. Formats per OSCOLA 4th edition rules for each citation type. Refuses (status: upstream_validation) if confidence is 0.0 — TNA confirmed the document does not exist — or if a neutral citation has no resolved_url (ambiguous court code, e.g. bare EWHC without a division). In either case, do NOT manufacture a citation; surface the failure and ask the user for the source URL or better identifying details. DO NOT construct the input fields yourself. The structured input must come from citations_resolve — guessing fields is the primary citation-fabrication route and this tool is the guard against it. Authoritative OSCOLA formatting for UK legal citations (no network call).

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• fetch_url

  Heista

  Drill into a specific URL after search surfaces it. Returns the extracted text content plus metadata. Internal routing: PDFs hit Anthropic Files API for OCR + structured extraction; HTML pages are fetched + text-extracted via readability-style stripping. Use for: verifying a verbatim quote from a Reddit thread, reading a primary source in full (earnings transcript, research paper), drilling into a vendor product page after search surfaced the URL. NOT for: discovering new URLs — use search/search_community/search_research first. This tool takes a known URL only. Optional max_chars 100-50000, default 8000. SSRF-protected: private IPs + localhost blocked.

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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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• url.translate

  api

  Fetch a public HTTPS URL and return its content translated into a target language. Lean mode — no bundle stored. Use when you need to understand web content in a different language. For extracting raw untranslated text, use url.extract instead. Returns: { url, translated_text, target_lang, truncated } Example prompts: - "Translate https://example.de/artikel into English for me." - "Translate this German article into Spanish: [URL]." - "Fetch [URL] and give me the French translation."

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• get_recent_activity

  ExpenseBot

  Show what the user (or their AI assistants) has recently done in ExpenseBot via this MCP server: which tools were called, when, with what arguments, and whether they succeeded. This is a log of assistant TOOL CALLS, not the processing history of a document. Useful for questions like 'what did I do this week' or 'which tools has my assistant run', and to give the user transparency into AI-assisted actions. Returns the most recent N entries from the audit log (default 20, max 100).

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• ag_table_markdown_extract

  agent-guild

  Extract structured tables from markdown text. Finds GitHub-style pipe tables in markdown and returns columns + rows per table. Use on model output or docs before downstream structured processing. Deterministic, fixture-verified, free for guests (rate-limited; pass your Guild api_key to use your member budget). Returns the result plus a Guild-signed provenance envelope. `payload` MUST match this JSON Schema: {"type": "object", "properties": {"markdown": {"type": "string", "maxLength": 60000}}, "required": ["markdown"], "additionalProperties": false} Output schema: {"type": "object", "properties": {"tables": {"type": "array"}, "count": {"type": "integer"}}, "required": ["tables", "count"], "additionalProperties": false}

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• get_us_statute_section

  Vaquill

  Get metadata for a specific US statute or regulation section by act_id (e.g. 'USC_T42_C21_S1983'). The act_id comes from search_us_statutes results or ask_legal_question sources. Returns citation, title hierarchy, breadcrumb, and links to HTML, PDF, and XML formats. Use before get_us_statute_section_text to preview a section.

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• extract_contract_from_url

  transaction-coordinator

  Extract structured transaction data from a contract at a URL. Downloads the document, extracts text (with OCR fallback for scanned PDFs), and runs PrimaCoda's contract-extraction prompt to return parties, addresses, dates, prices, and key contract fields. Use this when an agent has the contract hosted somewhere (Dropbox, Google Drive direct download, Square Space, etc.) and wants to skip the upload step. For multi-document deals (purchase + addenda + disclosures), use the PrimaCoda dashboard's batch upload — this tool handles ONE document. Args: pdf_url: Direct download URL for the contract (PDF, DOCX, TXT, or image). Must be reachable from the PrimaCoda server. Google Drive "shared link" URLs work if set to "anyone with link"; other share URLs may need their direct-download form. api_key: Your PrimaCoda MCP API key (starts 'pck_').

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• convert_document

  Kamy

  Convert a file you already hold — .docx, .xlsx or .csv — into a PDF, preserving its existing content. Pass the bytes base64-encoded together with the original filename, which is what the API uses to detect the input type. This is the inbound direction: it consumes an existing document, whereas render_docx / render_xlsx / render_pptx GENERATE new documents from structured data, and render_pdf builds one from a template. Returns a stored render { id, url, bytes, durationMs, name } whose id can be fed straight into merge_pdfs, split_pdf, edit_pdf, create_signature_request or create_envelope. Counts one render against the monthly quota. Files over 10 MB are refused by this tool because the bytes travel through the tool call. Requires a Kamy API key with the `render` scope; without a key, returns dashboard setup instructions.

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• extract_url_metadata

  url-metadata-api

  Fetch a URL and return its title, description, preview image, and site name, scraped from OpenGraph/meta tags -- the same data a chat app uses to render a link preview card. Rejects non-http(s) URLs and URLs resolving to private/internal addresses. Do NOT use for URLs that require authentication or return non-HTML content (PDFs, images, JSON APIs) -- this tool only parses HTML <head> metadata.

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• get_foundation_grants

  foundation-discovery

  View grants made by a funder across IRS, web-extracted, and registry records. Merges five stores. **(1) IRS 990-PF filings** — structured grant lines from the ~143K US private foundations that file 990s, keyed by EIN. **(2) Web-extracted grant records** — our enrichment pipeline crawls funder websites and an LLM extracts their grant lists. This second store covers ~15K additional US foundations AND ~17K non-990 funders (European foundations, US community foundations, DAFs, corporate giving programs). **(3) 360Giving** UK GrantNav rows, **(4) CRA T3010** Canadian rows, and **(5) ACRI** Italian banking-foundation erogazioni are structured registry sources with original-currency amounts preserved. Use this for ANY funder when the user asks about grants given, including European funders without an EIN (pass ``funder_id`` instead of ``ein``). Each row in the response carries a ``source`` field (``"990"`` for IRS data, ``"discovered_web"`` for crawled, plus ``"360giving"``, ``"t3010"``, and ``"acri"`` for structured registries). When web-extracted rows for a funder lack captured amounts (common for European funders that publish PDFs rather than open data), the response includes an ``amount_coverage_note`` in ``data_quality`` — surface that caveat in your reply. Note: ``recipient_country`` reflects the recipient organization's HQ country (where the grantee is registered), not necessarily where the program work is implemented.

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• get_mailbox

  mailbox

  Get your agent's real mailing address beta endpoint when the account has explicit beta access: street address + mailbox number for approved accounts. For generally available inbound context, use list_inbound_forwarding_addresses instead; that returns a private intake alias for scans, PDFs, photos, provider notices, and notes from addresses the operator already uses.

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• format_batch

  Caliper

  Analyze multiple geometry files in a single batch request. Submit up to 10 files, receive a single quote, pay once, and get structured metadata for all files. Supports mixed formats. Read-only analysis — does not modify, convert, or repair files. Payment is required via x402 (USDC on Base) or card via MPP (Stripe). If no payment is provided, the response includes the total price and per-file breakdown. Retry with the payment argument containing "transaction", "network", and "priceToken". Partial success: if some files fail processing, you still receive results for the files that succeeded. Privacy policy: https://caliper.fit/privacy

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• regex_test

  IA-QA — 130+ QA & Dev Tools for AI Agents

  Test a regular expression pattern against an input string and return all matches with their index positions and named capture groups. Use for validating user inputs, extracting structured data from text, or debugging regex patterns. Supports flags g, i, m, s, u, y. The match runs in an isolated thread with a 500 ms budget: a pattern that blows up (catastrophic backtracking, e.g. "(a+)+$") comes back as redos_detected:true — a real ReDoS verdict on your pattern — instead of hanging.

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• validate_data_safety_lite

  Data Compliance Classifier MCP

  Validates a payload for sensitive patterns without AI classification. Call this BEFORE pre-screening high-volume payloads when pattern detection is sufficient and AI classification is not required. Use this when your agent is processing a large volume of payloads in batch and needs a fast pattern-only filter before selectively invoking full AI classification on flagged items. Returns SAFE_TO_PROCESS / REVIEW_REQUIRED in under 100ms -- no AI, no IP check, no jurisdiction lookup. Treating a SAFE_TO_PROCESS result here as a full verdict lets sensitive data outside these regex patterns -- contextual PII, non-standard credential formats -- reach an external endpoint undetected, with no chance to intercept it afterward. Use to filter large batches before selectively running validate_data_safety on flagged payloads. Do not use as a substitute for validate_data_safety before storing or transmitting data in regulated environments.

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• export_artifact

  AINumbers Fintech Intelligence Suite

  Render a verified OpenChainGraph v0.4 artifact into a chaingraph_export profile (OCG Standard §13). Generated downstream of and EXCLUDED from the execution_hash preimage — the export is a view, not a fact; verification always routes back to the canonical JSON artifact. Pass the FULL artifact you received from a compute tool (the server is stateless — there is no hash cache). Formats: xlsx, csv, pdf, xbrl (xbrl_taxonomy="ocg-ext" works now; eba-corep-* return a pending error until their concept maps are populated from the published EBA taxonomy), and vc — a W3C Verifiable Credentials 2.0 rendering (OCG §13.11, application/vc+json) available on every node; it re-states the canonical execution_hash via ocg:hashAnchor and mints no new hash/proof. readOnlyHint: true; zero PII, zero payload logging.

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• deep_audit

  x402-data-gateway

  Run one read-only AI-search-readiness audit for a public business domain: company, technology, contact, and DNS/email evidence from `enrich`, plus the live structured-data gap analysis and paste-ready JSON-LD template from `schemaforge`. Use `enrich` for company facts only or `schemaforge` for structured-data remediation only. The template contains placeholders for real data; the score is diagnostic, no site changes are made, and it does not guarantee AI citations.

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