pdf-extract-mcp
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- FlicenseNot gradedqualityBmaintenanceEnables reading entire PDF documents into validated structured JSON, including tables, key-values, and markdown, with read-only extraction tools and layout reconstruction.-
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to convert PDFs and images into validated, structured JSON for invoices, receipts, contracts, and resumes, or into any caller-supplied JSON Schema, through five extraction tools. Each tool call pays per use in USDC over x402 from a wallet the caller supplies, with no signup or API key required.22 npmMIT
- AlicenseAqualityDmaintenanceEnables RAG over messy PDFs — extract, chunk, embed, and search scanned, multi-column, and table-heavy documents.6MIT
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- AlicenseNot gradedqualityCmaintenanceEnables conversion of PDFs, images, and messy text into schema-guaranteed JSON, with tools for schema inference, validation, and credit management.MIT
- AlicenseNot gradedqualityAmaintenanceExtracts text and tables from PDFs for AI agents via MCP, enabling structured data retrieval from invoices, reports, and statements.75 PyPI1MIT
TDQS
Scored across 3 tools
Each tool serves a clearly distinct purpose: listing supported schemas, extracting fields from a PDF, and validating extracted data against a schema. There is no overlap or ambiguity in tool responsibilities.
All tool names follow a consistent verb_noun pattern in snake_case: list_supported_document_types, extract_fields, validate_against_schema. The convention is uniform and predictable.
With only 3 tools, the set is at the lower end of the well-scoped range but still appropriate for a focused PDF extraction and validation server. Each tool earns its place without redundancy.
The tool surface covers the core workflow: discovering available schemas, extracting fields, and validating results. Minor gaps exist (e.g., no tool to add custom schemas or handle batch processing), but they are not critical for the primary purpose.