"Information or tools for code refactoring" matching MCP servers:

Enables semantic search over codebases using natural language queries, returning relevant code snippets with source locations. Integrates with Claude Code for automatic codebase exploration.

An MCP server that provides dynamic codebase context to Claude Code through tools like hybrid search, recent changes, and symbol definitions, enhancing AI-assisted coding with local RAG.

Enables agents to run semantic search across one or more local project directories by automatically maintaining a LAN-local Qdrant index with Ollama embeddings. Indexing, staleness checks, and incremental updates happen transparently, so users can query code by meaning without managing collections, chunks, or hashes.

Self-hosted hybrid code search MCP server with text, symbol, and semantic search layers. Runs locally, no third-party MCP servers, LSP, or SaaS.

MCP server for compressing AI embeddings by 5-7x using TurboQuant (PolarQuant + QJL), with tools to compress, decompress, estimate savings, and embed+compress vectors.

A multi-function Streamable HTTP MCP tool aggregation server that provides web search via Brave, Exa, and SearXNG with multi-key rotation and cross-provider fallback, and supports extensible tool families (URL fetch, code search, RAG) through a pluggable architecture.

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.

Enables clinical question answering via OpenEvidence with citation verification, including tools to ask questions, retrieve results, and follow up, plus an integrated skill to check citations against primary sources.

Multi-provider embedding MCP server with local Milvus for semantic code search. Works with Claude Code, Cursor, Windsurf, and other MCP-compatible IDEs.

Enables Claude to intelligently analyze and query codebases using knowledge graphs, supporting natural language code search, relationship discovery, and incremental updates.

Extends VS Code Copilot with MCP tools for persistent semantic memory, code indexing and RAG search, sandboxed Python/shell execution, web fetch/search, dynamic tool management via a web UI, and structured multi-step reasoning.

A semantic code search MCP server that enables natural language queries against your codebase, supporting features like related file discovery and context expansion, all running locally.

An MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.

An Operit-compatible adapter of the Exa MCP server, enabling web search, code search, and company research capabilities in AI assistants. It fixes MCP handshake compatibility issues, allowing tools like web_search_exa and web_fetch_exa to load and run reliably.

A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.

Enables AI assistants to search a curated database of 200+ AI tool cards with pricing, platforms, use cases, and source links, so users can ask which tool to use for a task and get grounded recommendations.

Enables AI coding assistants to semantically search codebases by meaning rather than exact text, with zero external daemons and fully local embeddings. Works out-of-the-box with Claude Code, Gemini CLI, Antigravity, and Cursor.

MCP server for GitHub code retrieval and reuse, using SQLite+FTS5 indexing and search history to enable search-first, requirements-refined code search from GitHub repositories.

An installable MCP server that reviews diffs/PRs against a target repository's own coding conventions, learned via RAG over its code, docs, and lint config, citing specific file:line sources.

Guides problem-solving by breaking down complex problems into steps and recommending appropriate MCP tools for each stage, with confidence scores and rationales for tool suggestions.