"How to run code using Claude Desktop" matching MCP servers:

Claude Context is an MCP plugin that adds semantic code search to Claude Code and other AI coding agents, giving them deep context from your entire codebase.

Enables Claude to index and retrieve context from codebases using self-hosted Milvus for semantic search, with hardened reliability and security for production use.

Enables searching and retrieving Claude Code conversation history via hybrid semantic and keyword search, allowing the agent to access its own past interactions.

MCP server that indexes Markdown files into a local SQLite vector store and provides semantic search tools for Claude Code.

Enables semantic code search over a local codebase using Qdrant vector embeddings and OpenAI embeddings, allowing natural language queries from MCP-compatible clients like Claude Desktop.

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 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.

Hardware-accelerated codebase mapping that indexes Git repositories into Postgres/pgvector and serves code search, relationships, and static analysis results via a stdio MCP server.

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

Enables Claude Desktop to search and query personal document collections (PDF, Word, Markdown, text) using semantic search and conversational AI with full context preservation across exchanges.

A persistent memory system for Claude using LanceDB, enabling semantic search, local embeddings, and tools to manage topics, memories, and todos across conversations.

Enables AI coding assistants to automatically scan, store, and query API endpoints from codebases, providing instant lookup and semantic search to reduce context switching and token consumption.

An MCP server that provides semantic search over local git repositories, enabling users to clone repositories, process branches, and search code through vectorized code chunks.

Enables Claude to recall and query past conversations through a local knowledge graph and vector index, with tools for searching memory, retrieving related entities, and adding new memories.

MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.

A semantic search MCP server for YouTube transcripts using OpenAI embeddings and ChromaDB, enabling natural language queries to find conceptually similar content beyond keyword matching.

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

A smart code retrieval tool based on Model Context Protocol that provides efficient and accurate code repository search capabilities for large language models.

Enables AI assistants to index and search codebases using semantic search powered by multiple embedding providers (OpenAI, VoyageAI, Gemini, Ollama) and vector database storage.

A persistent semantic memory system for Claude Code, using vector search and a judgment ledger to surface prior decisions and calibrate predictions.