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"A query related to PDFs or PDF files" matching MCP servers:

  • F
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    quality
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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
    license
    A
    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
    license
    A
    quality
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    maintenance
    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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    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
  • F
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    quality
    B
    maintenance
    Enables agents to perform controlled enterprise data queries through semantic intent, with runtime validation of statistics, filters, granularity, permissions, and physical bindings. Exposes tools like semantic_query for safe, fail-closed access to data horizons and capabilities.
  • A
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    quality
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    maintenance
    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
    D
    maintenance
    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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    quality
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    maintenance
    Builds searchable SQLite databases from PDFs, preserving inline image locations for AI agents to discover and caption visual content. Supports full-text search over text, image placeholders, and saved captions.
    1
    MIT
  • A
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    quality
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    maintenance
    Enables RAG over messy PDFs — extract, chunk, embed, and search scanned, multi-column, and table-heavy documents.
    MIT
  • F
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    quality
    D
    maintenance
    A Cloudflare Worker that transforms Cloudflare AI Search (AutoRAG) instances into an MCP server for querying documentation. It enables AI models to search and retrieve relevant information from custom document sets stored in R2 buckets.
    17
  • F
    license
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    quality
    B
    maintenance
    Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.
    4