PDF RAG MCP Server
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- AlicenseNot gradedqualityDmaintenanceEnables AI-driven PDF document processing including PDF to Markdown conversion, intelligent text and table extraction, image extraction, format conversion between PDF/Word/Markdown, batch processing, and fuzzy search - optimized for LLM context and RAG workflows.2MIT
- FlicenseNot gradedqualityDmaintenanceEnables AI-powered querying of PDF documents using hybrid retrieval (BM25 + vector search) and retrieval-augmented generation, returning structured answers with source citations and confidence scores.-
- AlicenseNot gradedqualityDmaintenanceEnables intelligent search and question-answering over PDF documents using semantic similarity and keyword search. Supports OCR for scanned PDFs, persistent vector storage with ChromaDB, and maintains source tracking with page numbers.6MIT
- AlicenseNot gradedqualityBmaintenanceEnables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.MIT
- FlicenseNot gradedqualityDmaintenanceEnables processing and analysis of large PDF files through text extraction, search functionality, and intelligent chunking strategies. Provides comprehensive PDF operations including metadata retrieval, page-range text extraction, and content search with contextual results.-
- AlicenseAqualityDmaintenanceEnables reading, searching, and metadata extraction from PDF files without loading the entire content into the context window. It provides efficient tools for text cleaning, page-specific extraction, and context-aware search results.343 npm1MIT
TDQS
Scored across 6 tools
Each tool targets a distinct operation: ingest, search, page retrieval, table extraction, listing, and deletion. There is no meaningful overlap between tool purposes, so an agent can clearly select the right tool.
All tools follow a consistent pdf_ prefix with verb-first naming, such as pdf_ingest, pdf_search, and pdf_get_page. The pattern is predictable and uniformly snake_case throughout.
Six tools is well-scoped for a PDF RAG server, covering ingestion, retrieval, and document management without unnecessary redundancy. Each tool earns its place in the set.
The tool surface covers the full PDF RAG lifecycle: ingest, search, retrieve page context, extract tables, list documents, and delete. There are no obvious dead ends or missing core operations for the stated purpose.