"How to work with local files" matching MCP servers:

Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.

Provides tools for searching and reading local files, supporting metadata filters, PDF full-text keyword search, and sandboxed file access.

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.

Persistent local semantic memory for MCP tools, enabling storing and searching notes by meaning using local embeddings and SQLite.

A TypeScript MCP server that provides secure, read-only access to a locally hosted MySQL database for AI-agent workflows, with configurable policies and support for multiple MCP clients.

Enables searching and retrieving documents from a local folder to ground LLM answers in your files.

A local-first document retrieval MCP server that enables AI coding tools like Codex to search private local documents via semantic search and keyword boost, supporting ingestion of PDF, DOCX, TXT, Markdown, and HTML files.

MCP server that enables local hybrid semantic and keyword search over private PDF, DOCX, Markdown, and text documents without sending data to embedding APIs.

A fully local RAG MCP server for semantic code search and code intelligence, using AST-level chunking and hybrid search to pinpoint functions, classes, and APIs. No cloud, no API keys, zero setup.

Generates DeepWiki-style documentation for private code repositories with RAG-based Q\&A capabilities, semantic code search, and multi-language AST parsing. Supports local LLMs (Ollama) or cloud providers for privacy-focused codebase analysis.

Local RAG knowledge base for Qwen Code, enabling document indexing and semantic search via MCP tools. Supports metadata filtering and document retrieval without external dependencies.

Provides OpenCode with adaptive long-term memory by storing preferences, lessons, and usage history locally in SQLite, enabling AI to remember and adapt to users through context-based learning.

A local MCP server that indexes files in a directory using Gemini Embedding 2, enabling AI agents to perform semantic search over local documents.

"primitive" RAG-like web search model context protocol server that runs locally. ✨ no APIs ✨

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

A read-only MCP server that provides document awareness for agents by parsing local files into structured profiles, blocks, chunks, and search results, enabling agents to understand and cite document content without dealing with raw file formats.

A local MCP server that indexes codebases using Tree-sitter AST parsing and LanceDB, enabling AI models to search across repositories with sub-second latency.

Enables storing and retrieving text passages based on semantic meaning using local embeddings (Ollama) and vector storage (ChromaDB), allowing conversational memorization and retrieval of information.

A local MCP server that allows AI systems to search and retrieve information from a custom knowledge base generated from markdown files. It provides tools for natural language text search, category browsing, and specific content chunk retrieval.

A local-first MCP server for PageIndex — the vectorless, reasoning-based RAG framework. It lets local AI agents index and query local PDF and Markdown documents through a self-hosted PageIndex installation, without requiring any PageIndex cloud API key.