"MCP servers for reading and parsing PDF, Word, and PowerPoint documents" matching MCP servers:

Enables AI assistants to query PDF documents by ingesting them into a vector database and generating answers grounded in the actual documents.

Enables agents to search local component-manual corpora via hybrid semantic, full-text, and substring retrieval with table recognition and OCR fallback for scanned pages. Provides tools for literal/regex grepping, reading single or multiple pages, navigating bookmarks and outlines, listing categories and indexed files, and checking index and dependency status.

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.

Enables intelligent ingestion and querying of PDF, Markdown, and text files using hybrid search that combines keyword matching and semantic embeddings with citations.

A document knowledge base system that enables users to upload PDFs and query them semantically through a web interface or via the Model Context Protocol, allowing integration with AI tools like Cursor.

A Model Context Protocol server that enables intelligent document search and retrieval from PDF collections, providing semantic search capabilities powered by OpenAI embeddings and ChromaDB vector storage.

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.

Enables 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.

A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.

Upload documents (Word, Excel, PDF, PowerPoint) to a vector RAG store and perform semantic search with page-level citations. Queries are free; ingestion costs credits at break-even pricing.

Upload Word, Excel, PDF, or PowerPoint documents to a vector RAG store with vision-model extraction, then search semantically and retrieve chunks with page numbers for precise citations.

An integration server implementing the Model Context Protocol that enables LLM applications to interact with Milvus vector database functionality, allowing vector search, collection management, and data operations through natural language.

Provides semantic search and complete document retrieval by ID through OpenAI's Vector Store API, letting deep research agents access company-specific knowledge and tools.

Enables Claude Desktop to search and query personal document collections (PDF, Word, Markdown, text) using semantic search and conversational AI with full context preservation across exchanges.

MCP RAG Server is a Python MCP server that indexes documents in multiple formats (Markdown, text, PowerPoint, PDF) using multilingual-e5-large embeddings and enables vector search for retrieval-augmented generation.

Enables real-time indexing and semantic search of local documents (PDF, Word, text, Markdown, RTF) using vector embeddings and local LLMs. Monitors folders for changes and provides natural language search capabilities through Claude Desktop integration.

Enables users to ingest documents into a PostgreSQL/pgvector knowledge base, run semantic search over them, and get grounded answers through a retrieval-augmented generation pipeline backed by a free LLM. It also lets clients spin up specialized AI agents on demand and exposes knowledge-base stats and configuration as resources for tutoring workflows.

A local-first Graph-RAG system combining ChromaDB with metadata-based graph relationships and Gemini 2.5 Flash for intelligent Q&A over Obsidian vaults, supporting MCP clients like Claude Desktop, Cursor, and Raycast.

A server implementation that allows secure communication between MCP clients and privateGPT, enabling users to chat with privateGPT using knowledge bases and manage sources, groups, and users through a standardized Model Context Protocol.

Enables retrieval-augmented generation (RAG) by indexing and searching through documents (Markdown, text, PowerPoint, PDF) using vector embeddings with multilingual-e5-large model and PostgreSQL pgvector. Supports contextual chunk retrieval and incremental indexing for efficient document management.

RAG-powered document search server that enables semantic search across large collections of legal and business documents (PDF, Word, Excel, PowerPoint) using local embeddings with no API costs.

MCP server for semantic search over PDF documents using RAG, pgvector, and OpenAI embeddings. Includes a web app for document upload and management.

A local-first MCP server that enables semantic search over PDF and DOCX documents using structure-aware parsing and vector storage. It allows users to query their local knowledge base through Claude Code without cloud dependencies or GPU requirements.

MCP server that enables semantic search over PDF documents using RAG with ChromaDB and OpenAI embeddings.

MCP server enabling Claude Desktop to answer questions from local Word and PDF documents by searching a vector index built from their contents.

Munin is a high-performance, pragmatic memory layer for AI agents (Cursor, Claude Code, OpenClaw, Gemini CLI,...). Unlike other solutions, Munin focuses on developer productivity with: * Multi-Project Support: Isolate memories into separate "brains" (Context Cores). * GraphRAG: Automatically builds a knowledge graph from your context. * Sub-200ms Search: Blazing fast Hybrid & Semantic

Indexes PDF documents into Qdrant and exposes semantic search as MCP tools, enabling RAG-based interactions with your documents.

A local Retrieval-Augmented Generation system that enables users to ingest markdown files into a FAISS-powered vector knowledge base for semantic search. It provides tools for document indexing and context retrieval to support informed LLM queries without external dependencies.

MCP server for OpenAI Vector Store API, managing vector stores, files, file batches, and semantic search.

An MCP server that enables LLMs to perform semantic and fulltext searches within Neo4j while executing complex, search-augmented Cypher queries for GraphRAG applications. It provides tools for database schema discovery and supports multi-provider embeddings to facilitate advanced graph traversals.