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79,420 servers. Updated
20 Best PDF MCP Servers: compared and ranked, August 2026Ranked from 1,240 matching servers on stars, growth, downloads and maintenance. Updated .

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"Tools or methods to convert PDFs to Markdown using OCR" matching MCP servers:

  • F
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    quality
    C
    maintenance
    MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.
  • A
    license
    A
    quality
    D
    maintenance
    A Model Context Protocol (MCP) server that provides a local-first RAG engine for your markdown documents. It uses a file-based Milvus vector database to index your notes, enabling LLMs to perform semantic search and retrieve relevant content from your local files.
    3
    56
    Apache 2.0
  • A
    license
    Not graded
    quality
    A
    maintenance
    MCP server for compressing AI embeddings by 5-7x using TurboQuant (PolarQuant + QJL), with tools to compress, decompress, estimate savings, and embed+compress vectors.
    MIT
  • F
    license
    Not graded
    quality
    D
    maintenance
    A Model Context Protocol server that provides RAG capabilities for markdown documents using Qdrant for vector storage and Ollama for embeddings, enabling semantic search and document ingestion directly from Cursor IDE.
  • A
    license
    D
    quality
    C
    maintenance
    A lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.
    1
    2
    MIT
  • A
    license
    Not graded
    quality
    D
    maintenance
    A Retrieval Augmented Generation system that enables AI assistants to perform semantic searches and manage document indices for markdown files. It supports PostgreSQL with pgvector and integrates both Google Gemini and Ollama for intelligent embedding generation.
    1
    MIT
  • A
    license
    Not graded
    quality
    Not graded
    maintenance
    Enables semantic search across Apple Mail, Messages, Calendar, and Contacts on macOS using natural language queries. All processing happens locally with privacy-first vector indexing for fast similarity search.
  • A
    license
    Not graded
    quality
    B
    maintenance
    Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.
    MIT
  • A
    license
    B
    quality
    D
    maintenance
    Enables Claude to interact with core AWS services like S3, EC2, RDS, and CloudWatch, along with a generic SDK wrapper for any AWS operation. It also supports cost monitoring and optional vector store capabilities for document ingestion and search.
    10
    3
    The Unlicense
  • A
    license
    Not graded
    quality
    D
    maintenance
    Enables 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.
    5
    MIT
  • A
    license
    A
    quality
    A
    maintenance
    Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.
    9
    3,968
    371
    MIT
  • F
    license
    A
    quality
    C
    maintenance
    Enables Claude to search a local hybrid retrieval index of research papers and ingest new PDFs, providing research-paper memory queryable directly through natural language.
    2
  • A
    license
    A
    quality
    A
    maintenance
    Enables AI agents to search local Markdown documents using natural language, with automatic indexing and section-level retrieval.
    9
    2
    1
    MIT
  • A
    license
    A
    quality
    D
    maintenance
    Provides local Retrieval-Augmented Generation (RAG) capabilities using Ollama for embeddings and ChromaDB for vector storage. It enables users to ingest and perform semantic searches across PDF, Markdown, and TXT documents within MCP-compatible clients.
    4
    245
    MIT
  • F
    license
    B
    quality
    D
    maintenance
    Enables semantic search and contextual conversations with your Calibre ebook library using vector-based RAG technology. Supports project-based organization, multi-format book processing, and OCR capabilities for enhanced content extraction and retrieval.
    7
    2
  • A
    license
    Not graded
    quality
    D
    maintenance
    Vectorize MCP server for advanced retrieval, Private Deep Research, Anything-to-Markdown file extraction and text chunking.
    73
    111
    MIT
  • A
    license
    Not graded
    quality
    D
    maintenance
    An MCP server that gives AI assistants the ability to remember user information (preferences, behaviors) across conversations using vector search technology.
    16
    MIT