"AI for creating and modifying Office documents" matching MCP servers:

Exposes document ingestion, retrieval (vector, vectorless, hybrid), and multi-turn chat tools for a LangGraph-powered RAG pipeline with streaming answers.

Exposes document retrieval as an MCP tool, enabling LLMs to search a local vector store of markdown documents. Includes a retrieval evaluation harness to measure hit rate and MRR.

Enables Claude Code to search and retrieve from a local knowledge base of markdown notes using hybrid semantic+keyword search, keeping data entirely offline.

Enables AI agents to store, retrieve, and manage contextual knowledge across sessions using semantic search with PostgreSQL and vector embeddings. Supports memory relationships, clustering, multi-agent isolation, and intelligent caching for persistent conversational context.

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

MCP server to perform semantic and keyword searches across AI Nike-chan's public X posts and official website, with optional AI Gateway integration and vector index hosting on Vercel Blob.

Enables Claude Desktop to search private documents using Azure AI Search and perform web searches with Bing, providing AI-enhanced results with source citations through Azure AI Agent Service or direct Azure AI Search integration.

Provides a local-first secure memory store with encrypted payloads, entity extraction, and vector persistence, exposing read, write, and delete tools with granular capability controls.

MCP server for Vectros, a typed multi-tenant record store with hybrid search and citation-grounded RAG, enabling agents to query, search, and ask questions over their own indexed data.

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

Enables semantic search, image search, and cross-modal search functionalities through integration with Jina AI's neural search capabilities.

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.

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.

An MCP-compatible RAG backend using LangGraph and FastAPI, enabling chaining of AI model logic with document context search.

Enables querying medical documents stored in Qdrant using semantic retrieval, allowing users to search PubMed articles through the Model Context Protocol.

Enables LLMs to interact with Zvec vector database through tools for collection management, document operations, vector search, and AI-powered embeddings.

A local-first AI backend that combines Model Context Protocol, Retrieval-Augmented Generation, and an agent runtime to answer questions from a private knowledge base.

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.

A local-first MCP server for indexing and searching codebases using PostgreSQL and Ollama embeddings, ensuring complete privacy and zero API costs.

A local-first semantic search server for documents, supporting PDFs, Office files, and text/markdown, enabling natural language search via the Model Context Protocol (MCP).