Extract specific structured data from web pages using LLM-powered extraction. Use a prompt or schema to get details like prices, names, and descriptions.
MIT
271,551 tools. Last updated 2026-07-08 03:23
"Tools for Extracting Structured Data from PDFs Using OCR" matching MCP tools:
- Extract structured data from web pages using LLM capabilities. Define specific information to retrieve with custom prompts and JSON schemas for organized output.MIT
- Verify identity documents by submitting front and optional back images. Get structured OCR data and authenticity checks for fraud prevention.MIT
- Extract full text content, metadata, and structured information from specific web URLs for detailed content analysis and data retrieval.MIT
- Extract structured data or text from web pages using specific instructions to target and retrieve information from the current page.Apache 2.0
- Extract structured content from PDFs, images, and Office files while preserving original formatting and layout using Upstage AI's document digitization API.MIT
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Structured-shofficial
Alicense-qualityDmaintenanceMCP 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.Last updated2Apache 2.0- Alicense-qualityCmaintenanceProvides AI agents with data validation, transformation, and normalization capabilities, including JSON schema validation, CSV processing, data normalization, text cleaning, and dataset merging.Last updated127MIT
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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).
- Extract text from USPTO petition documents using hybrid extraction: free PyPDF2 for text-based PDFs, Mistral OCR for scanned documents. Analyze legal arguments, issues, and patterns in petition decisions.MIT
- Extract text from local images and PDFs with Apple Vision OCR, returning plain text or structured blocks with bounding boxes. Works offline on macOS.MIT
- Extracts text from scanned or image-only PDFs using Tesseract OCR, returning per-page content and confidence scores. Use when standard PDF reading fails due to missing text layer.MIT
- Extract tables from tagged PDFs: walks structure tree to pull cell text, outputting as Markdown or JSON for structured data.MIT
- Retrieve all resources attached to a note, including OCR text from images and PDFs, to reveal content inside attachments.MIT
- Extracts plain text from vault PDFs, returning per-page content, full text, and metadata. Supports page range selection and flags image-only PDFs for OCR.MIT
- Extract parties, key dates, financial terms, and essential clauses from contract PDFs into structured JSON.MIT
- Run OCR on scanned PDFs to add a searchable text layer using Tesseract, making the text copyable and searchable.MIT
- Extract structured data from web pages using LLM capabilities. Define specific information to retrieve with custom prompts and JSON schemas.MIT
- Extract structured data from web pages using LLM capabilities. Define specific information to retrieve like product details or pricing through custom prompts and schemas.MIT
- Extract text from PDFs and images as structured Markdown. Handles complex layouts, tables, handwriting, and math notation. Pay per page with Bitcoin Lightning.MIT
- Extract text from images (JPEG/PNG) or single-page PDFs using Yandex Vision OCR. Supports printed, handwritten, table, and markdown models with language selection.MIT
- Read text, images, and tables from specific PDF pages with support for page ranges and OCR for scanned content.MIT
- Extract text from a local PDF (base64) and convert to markdown. Works only for text-based PDFs; scanned documents return empty.AGPL 3.0