"Searching for local information or documents" matching MCP servers:

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.

A Model Context Protocol (MCP) server that enables AI applications to access and analyze local code repositories without manual uploads, providing file listing, content reading, code searching, and project structure analysis capabilities.

Enables AI to precisely use local Vue components by extracting their Props, Events, and Slots via AST analysis.

A Model Context Protocol server that helps programmers understand code by providing explanations, tech stack analysis, and best practice suggestions through prompt templates.

A local, zero-dependency MCP server that cuts coding-agent round-trips by aggregating search, read, edit, exec, git, and savings tools into single, validated calls, with cost tracking and no data leaving the machine.

Safe local MCP server for Windows to list, read, search, patch, backup, and verify code files in allowed folders, with Git integration and dry-run diffs.

Enables AI assistants to intelligently search and explore local file systems using native Unix commands (ripgrep, find, ls) with token-optimized output, automatic pagination, and multi-layer security validation.

Provides Claude Code with local semantic search and indexing of your codebase using AST-aware chunking and hybrid search, enabling deep code understanding without sending data to the cloud.

Provides a read-only MCP Streamable HTTP server for inspecting a configured local workspace, including directory listing, text-file reading, and literal or regex search.

Provides semantic code search capabilities that run 100% locally using EmbeddingGemma embeddings. Enables finding code by meaning across 15 file extensions and 9+ programming languages without API costs or sending code to the cloud.

An MCP server that indexes local Python projects into a SQLite database to enable efficient symbol searching and dependency tracking. It allows users to find function or class definitions, trace module imports, and read file contents through natural language interfaces.

Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.

MCP server connecting Claude Code to LM Studio, delegating token-expensive tasks to a local model while keeping the cloud model in control. It reduces cloud context usage by reading files locally and returning only the processed results.

Enables AI assistants to access and interact with Cursor/VS Code Local History data for file recovery and enhanced context awareness. Provides tools to browse file history, search across snapshots, and restore previous versions of files.

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.

A secure MCP server that exposes local repository context to ChatGPT/Codex with read-only access, path validation, and no generic shell.

Delegates heavy, repetitive, and verifiable tasks like PDF extraction, code analysis, and log processing to a local LLM to reduce token consumption for frontier AI models, while keeping decision-making with the main AI.

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.

Provides AI agents with ground-truth knowledge of Strapi projects by reading real schema files, enabling accurate queries and safe refactoring operations.

Bridges local LLMs running in LM Studio with MCP clients like Claude Desktop to perform reasoning and analysis tasks while keeping sensitive data private. It features a suite of tools for local code review, privacy scanning, and content transformation using auto-discovered local models.