A
licenseA
qualityA
maintenanceA Model Context Protocol server that enables AI agents to read, search, and extract content from PDF files.
13
111
MIT
72,963 servers. Updated
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"Official tool for reading and analyzing PDF files from MCP" matching MCP servers:
- AlicenseCqualityFmaintenanceA custom MCP tool that integrates Perplexity AI's API with Claude Desktop, allowing Claude to perform web-based research and provide answers with citations.16MIT
- Alicense-qualityBmaintenanceEnables AI agents to access and search up-to-date Solana documentation, get canonical spec references, and fix Anchor/Pinocchio Solana programs via MCP tools.1579MIT
- AlicenseAqualityBmaintenanceA 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.10MIT
- FlicenseAqualityCmaintenanceA local RAG MCP server for PDF development experience, enabling document ingestion, semantic search, and Q\&A with source citations using TF-IDF and cosine similarity.3
okraPDF PDF MCP serverofficial
AlicenseAqualityCmaintenanceThis 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.27MIT- Alicense-qualityDmaintenanceA 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
- Alicense-qualityDmaintenanceEnables AI assistants to query PDF documents by ingesting them into a vector database and generating answers grounded in the actual documents.111MIT
- Alicense-qualityDmaintenanceAn MCP (Model Context Protocol) server that chains calls to other MCP tools, reducing token usage by allowing sequential tool execution with result passing10958MIT
- Flicense-qualityBmaintenanceA universal MCP tool gateway that manages multiple remote tool processes, enabling LLMs to dynamically invoke web scraping, summarization, and knowledge retrieval through standardized Function Calling.
- Flicense-qualityBmaintenanceEnables agents to extract clean structured JSON tables from any PDF with per-cell citations to the source page.
- Flicense-qualityCmaintenanceExposes search over internal documents and company lookup via authenticated API, enabling LLMs to access firm files and data.
- Flicense-qualityDmaintenanceEnables intelligent ingestion and querying of PDF, Markdown, and text files using hybrid search that combines keyword matching and semantic embeddings with citations.2
- AlicenseAqualityBmaintenanceAn MCP proxy that embeds tool definitions and returns only the top-k semantically relevant tools per query, reducing context usage and routing calls to upstream MCP servers.2MIT
- AlicenseAqualityAmaintenanceGraph-based tool retrieval for LLM agents. Builds a tool graph from OpenAPI/MCP specs and retrieves multi-step workflows via hybrid search (BM25 + graph traversal + embedding), recovering accuracy from 12% to 82% with 79% fewer tokens. Also works as an MCP Proxy to aggregate multiple servers behind 3 meta-tools.616MIT
- AlicenseAqualityCmaintenanceA Model Context Protocol server that provides Claude with a dedicated space for structured thinking during complex problem-solving tasks, helping improve its reasoning capabilities.12916MIT
- Alicense-qualityDmaintenanceEnables RAG over messy PDFs — extract, chunk, embed, and search scanned, multi-column, and table-heavy documents.MIT
- Alicense-qualityCmaintenanceMCP server that enables persistent, hybrid, local memory for LLM agents, with vector + BM25 search, knowledge graph, and policy-driven retention, providing token-budgeted context injection for AI assistants.MIT
- Alicense-qualityCmaintenanceEnables AI agents to download, index, and semantically search PDF research papers using 8 MCP tools.2GPL 3.0
- Flicense-qualityCmaintenanceMCP 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.
