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"Tools for converting PDF files to Markdown format" matching MCP servers:

  • F
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    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
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    An MCP server that enables users to download webpages as markdown files using r.jina.ai service, with features for configurable download directories and automatic date-stamped filenames.
    5
    7
    54
    MIT
  • A
    license
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    quality
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    maintenance
    A local-first MCP server that ingests PDFs, extracts structure, and provides semantic search and sequential navigation tools for AI clients to query and learn from documents.
    10
    MIT
  • A
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    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
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    quality
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    This MCP server enables AI agents to view PDFs as accessible HTML with bounding-box citations, and provides tools for layout-aware parsing, schema extraction, cross-document Q&A, and PDF rendering.
    27
    MIT
  • A
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    quality
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    A Model Context Protocol (MCP) server that provides powerful RAG (Retrieval-Augmented Generation) capabilities for PDF documents. This server uses ChromaDB for vector storage, sentence-transformers for embeddings, and semantic chunking for intelligent text segmentation.
    MIT
  • A
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    Provides semantic search over markdown documentation using RAG, allowing natural language queries and integration with MCP clients.
    1
    MIT
  • F
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    quality
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    Enables managing and searching markdown notes with semantic search, question answering, and note generation, and provides an MCP server for GitHub Copilot integration.
    4
  • F
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    quality
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    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
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    Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.
    3
    27
    Apache 2.0
  • A
    license
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    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
  • A
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    quality
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    Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.
    1
    MIT