"A local-only document search tool" matching MCP servers:

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.

A tool discovery MCP server that integrates with Azure DevOps wikis and code-graph-rag to match natural language goals to tools, enabling tool composition and proxy testing.

A sophisticated server that enables AI assistants to automatically analyze codebases and generate comprehensive, professional documentation.

Enables AI agents to generate and retrieve a contextual document for a git branch, summarizing repository state, changes, and freshness, so they can understand the branch without scanning the entire codebase.

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.

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.

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.

Enables AI assistants to leverage Qwen's code analysis capabilities with large context windows, supporting file/directory analysis, sandbox execution, and multiple approval modes for safe code operations.

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.

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.

Turns local code repositories into a queryable dependency graph built from AST parsing and git co-edit history, then exposes it alongside an Obsidian vault as persistent memory. Enables context retrieval, impact analysis of proposed changes, and curation of durable prose knowledge through full-text search and Personalized PageRank classification—all fully offline without API keys or embeddings.

Integrates the Atlassian Rovo Dev CLI with the Model Context Protocol, allowing AI assistants to perform deep code analysis using Rovo Dev's large context capabilities. It enables features like repository-wide queries, file-specific analysis, and specialized coding modes through a standard MCP interface.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

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 semantic code search across indexed code folders using vector embeddings, with support for multiple embedding providers and automatic file watching. Provides an admin UI and integrates with MCP clients for natural language code queries.

Semantic + lexical code search as an MCP server. Agents query in natural language and get back ranked file:line ranges to read precisely.

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.