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271,718 tools. Last updated 2026-07-08 04:44

"Tools for Extracting Structured Data from PDFs Using OCR" matching MCP tools:

  • 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, parse_invoice (prebuilt schema) may be simpler. For general summarization, use summarize_document 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."
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  • 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.
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  • Run an Agent402 tool by slug (find slugs with search_tools). The 1177 pure-CPU tools execute free on this hosted connector (rate-limited). Wallet-only tools (live search, browser rendering, PDFs, durable memory) return instructions for paid access instead.
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  • Retrieve / download / get the file for a digital product after the user paid for it. Use after `pay_merchant` succeeds for digital goods (PDFs, ebooks, cheatsheets, datasets). Pass the on-chain `txHash` from `pay_merchant` OR a Coal checkout `sessionId`. Returns a verified download URL the user can click. Supported product slugs: `0g-cheatsheet` (The 0G Builder's Cheatsheet, $0.10).
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  • 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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  • Extract structured information from web pages using LLM capabilities. Supports both cloud AI and self-hosted LLM extraction. **Best for:** Extracting specific structured data like prices, names, details from web pages. **Not recommended for:** When you need the full content of a page (use scrape); when you're not looking for specific structured data. **Arguments:** - urls: Array of URLs to extract information from - prompt: Custom prompt for the LLM extraction - schema: JSON schema for structured data extraction - allowExternalLinks: Allow extraction from external links - enableWebSearch: Enable web search for additional context - includeSubdomains: Include subdomains in extraction **Prompt Example:** "Extract the product name, price, and description from these product pages." **Usage Example:** ```json { "name": "firecrawl_extract", "arguments": { "urls": ["https://example.com/page1", "https://example.com/page2"], "prompt": "Extract product information including name, price, and description", "schema": { "type": "object", "properties": { "name": { "type": "string" }, "price": { "type": "number" }, "description": { "type": "string" } }, "required": ["name", "price"] }, "allowExternalLinks": false, "enableWebSearch": false, "includeSubdomains": false } } ``` **Returns:** Extracted structured data as defined by your schema.
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Matching MCP Servers

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    MCP server providing managed persistent memory for AI agents. Read and write structured state across sessions, tools, and restarts at 1000+ requests per second, with no infrastructure to self-host or operate.
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    Apache 2.0

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  • A fully autonomous, Agent-to-Agent (A2A) patent data marketplace powered by the Model Context Protocol (MCP) and A2A standards. This server provides highly structured, AI-optimized JSON patent datasets curated for autonomous R&D agents, LLMs, and Quants. Currently exclusively hosting AI-ready patents from IPC/CPC Sections G (Physics & Computing) and H (Electricity).

  • Autonomous A2A marketplace providing AI-ready, structured USPTO patent JSON datasets. Features IPC/CPC Sections G (Physics/Computing, e.g., G01 Sensors, G06 AI/ML) and H (Electricity, e.g., H01 Semiconductors, H04 5G). Enables instant M2M data delivery via automated on-chain payment verification. Networks: Base (USDC), Polygon (USDC), Oasis (ROSE).

  • Autonomous web research agent. This is a separate AI agent layer that independently browses the internet, searches for information, navigates through pages, and extracts structured data based on your query. You describe what you need, and the agent figures out where to find it. **How it works:** The agent performs web searches, follows links, reads pages, and gathers data autonomously. This runs **asynchronously** - it returns a job ID immediately, and you poll `firecrawl_agent_status` to check when complete and retrieve results. **IMPORTANT - Async workflow with patient polling:** 1. Call `firecrawl_agent` with your prompt/schema → returns job ID immediately 2. Poll `firecrawl_agent_status` with the job ID to check progress 3. **Keep polling for at least 2-3 minutes** - agent research typically takes 1-5 minutes for complex queries 4. Poll every 15-30 seconds until status is "completed" or "failed" 5. Do NOT give up after just a few polling attempts - the agent needs time to research **Expected wait times:** - Simple queries with provided URLs: 30 seconds - 1 minute - Complex research across multiple sites: 2-5 minutes - Deep research tasks: 5+ minutes **Best for:** Complex research tasks where you don't know the exact URLs; multi-source data gathering; finding information scattered across the web; extracting data from JavaScript-heavy SPAs that fail with regular scrape. **Not recommended for:** - Single-page extraction when you have a URL (use firecrawl_scrape, faster and cheaper) - Web search (use firecrawl_search first) - Interactive page tasks like clicking, filling forms, login, or navigating JS-heavy SPAs (use firecrawl_scrape + firecrawl_interact) - Extracting specific data from a known page (use firecrawl_scrape with JSON format) **Arguments:** - prompt: Natural language description of the data you want (required, max 10,000 characters) - urls: Optional array of URLs to focus the agent on specific pages - schema: Optional JSON schema for structured output **Prompt Example:** "Find the founders of Firecrawl and their backgrounds" **Usage Example (start agent, then poll patiently for results):** ```json { "name": "firecrawl_agent", "arguments": { "prompt": "Find the top 5 AI startups founded in 2024 and their funding amounts", "schema": { "type": "object", "properties": { "startups": { "type": "array", "items": { "type": "object", "properties": { "name": { "type": "string" }, "funding": { "type": "string" }, "founded": { "type": "string" } } } } } } } } ``` Then poll with `firecrawl_agent_status` every 15-30 seconds for at least 2-3 minutes. **Usage Example (with URLs - agent focuses on specific pages):** ```json { "name": "firecrawl_agent", "arguments": { "urls": ["https://docs.firecrawl.dev", "https://firecrawl.dev/pricing"], "prompt": "Compare the features and pricing information from these pages" } } ``` **Returns:** Job ID for status checking. Use `firecrawl_agent_status` to poll for results.
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  • Parse a file using Firecrawl's /v2/parse endpoint. In local/non-cloud MCP mode, this tool reads filePath from the MCP server filesystem and posts multipart data to the configured self-hosted FIRECRAWL_API_URL, preserving the existing direct-read behavior. In hosted CLOUD_SERVICE mode, this tool is a two-call flow because hosted MCP cannot read your local filesystem: 1. Call with filePath, contentType, parse options, and optional declaredSizeBytes. The hosted server mints a short-lived upload URL and returns a safe local curl PUT command plus nextToolCall. 2. Run the returned curl command locally, then call firecrawl_parse again with uploadRef and the desired parse options. The hosted server calls /v2/parse server-side with your session credential. **Best for:** Extracting content from a local document (PDF, Word, Excel, HTML, etc.); pulling structured data out of a file with JSON format; converting binary documents into markdown for downstream reasoning. **Not recommended for:** Remote URLs (use firecrawl_scrape); multiple files at once (call parse multiple times); documents that require interactive actions, screenshots, or change tracking — those aren't supported by the parse endpoint. **Common mistakes:** In hosted mode, do not pass both filePath and uploadRef. Phase 1 uses filePath only to generate upload instructions; phase 2 uses uploadRef only to parse server-side. **Supported file types:** .html, .htm, .xhtml, .pdf, .docx, .doc, .odt, .rtf, .xlsx, .xls **Unsupported options:** actions, screenshot/branding/changeTracking formats, waitFor > 0, location, mobile, proxy values other than "auto" or "basic". **Privacy:** Set `redactPII: true` to return content with personally identifiable information redacted. **CRITICAL - Format Selection (same rules as firecrawl_scrape):** When the user asks for SPECIFIC data points from a document, you MUST use JSON format with a schema. Only use markdown when the user needs the ENTIRE document content. **Handling PDFs:** Add `"parsers": ["pdf"]` (optionally with `pdfOptions.maxPages`) when parsing a PDF so the PDF engine is invoked explicitly. For very long documents, cap `maxPages` to keep the response within token limits. **Hosted phase 1 example:** ```json { "name": "firecrawl_parse", "arguments": { "filePath": "/absolute/path/to/document.pdf", "contentType": "application/pdf", "formats": ["markdown"], "parsers": ["pdf"], "zeroDataRetention": true } } ``` **Hosted phase 2 example:** ```json { "name": "firecrawl_parse", "arguments": { "uploadRef": "upload-ref-from-phase-1", "formats": ["markdown"], "parsers": ["pdf"], "zeroDataRetention": true } } ``` **Returns:** Phase 1 hosted upload instructions or a parsed document with markdown, html, links, summary, json, or query results depending on the requested formats.
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  • Extract text from PDFs and images as clean Markdown. Uses Mistral OCR — handles complex layouts, tables, handwriting, multi-column documents, and mathematical notation. Preserves document hierarchy in structured Markdown. 10 sats/page. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='extract_document' and quantity=pageCount for multi-page PDFs.
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  • Extract structured data from receipts, invoices, and financial documents. Uses a dual-model pipeline (Mistral OCR + Kimi K2.5) for high-accuracy extraction. Returns JSON with merchant, date, line items, totals, tax, currency, and expense category. Handles crumpled receipts, faded text, and multi-page invoices. 50 sats/page. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='extract_receipt'.
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  • List all generated reports with status and summary info. Returns an array of report objects with id, report_type, status, title, and summary. Use the report id with atlas_get_report for details or atlas_download_report to download completed PDFs. Free.
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  • Get detailed CV version including structured content, sections, word count, and audience profile. cv_version_id from ceevee_upload_cv or ceevee_list_versions. Use to inspect CV content before running analysis tools. Free.
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  • Add all ingredients from a saved recipe to the shopping list. Use when the user wants to shop for a specific recipe. Requires the recipe to have structured ingredient data (most recipes do after enrichment). Get recipe IDs from get_recipes first.
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  • 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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  • Generate realistic mock data from a JSON Schema. Supports all common types (string, number, integer, boolean, array, object, null), format hints (email, date, date-time, uri, uuid), enum, const, and nested schemas. Perfect for testing MCP tools with realistic data.
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  • As a Chief Human Resources Officer (CHRO), benchmark executive compensation packages against peer companies using public SEC filings and private compensation data from Equilar and Bloomberg. Inputs include executive name, title, company ticker, and peer group criteria. Outputs structured compensation metrics (base salary, bonus, equity, total compensation) with source attribution and confidence scores.
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  • Enables CHROs to benchmark their company's sabbatical policies against peer organizations using data from SHRM, Payscale, and Mercer. Inputs include company size, industry, and current policy details. Outputs structured comparison with cost impact analysis, eligibility criteria, and duration benchmarks. Ideal for strategic HR planning and policy optimization.
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  • Extract plain text from a PDF or image (base64-encoded). Use when you need raw text for downstream AI analysis (summarization, claim checking, structured extraction). For documents at a public URL, use extract_url instead (no base64 encoding needed). Returns: { pages: number, text: string } Example prompts: - "Extract the text from this scanned contract so I can search it." - "Give me the raw text from this PDF document." - "OCR this image and return the text content."
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  • Extract tables and forms as Markdown from a PDF or image (base64-encoded). Use when the document contains structured tabular data such as financial statements, data sheets, or forms. For plain prose documents, use extract_text instead. Returns: { pages: number, text: string } — text contains Markdown-formatted tables. Example prompts: - "Extract the tables from this financial statement." - "Pull the data table from this PDF into Markdown format." - "Get the tabular data from this form document."
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  • 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, parse_invoice (prebuilt schema) may be simpler. For general summarization, use summarize_document 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."
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