"A server for finding code examples on GitLab using semantic search" matching MCP servers:

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.

Indexes codebases using semantic embeddings for natural language search, enabling developers to find code with queries like 'how does authentication work'.

A tiny RAG-lite retrieval engine that indexes files on disk and provides semantic search via MCP, returning relevant text chunks (file, line, score) without generating answers.

Local offline semantic search over documents (txt, md, pdf, docx, pptx, csv). Indexes folders into a LanceDB vector database with multilingual embeddings and supports hybrid vector + keyword search via Reciprocal Rank Fusion. No API keys, no cloud, no Docker required.

Enables Claude to store and retrieve information with semantic search using Qdrant vector database, providing persistent memory for conversations, code, and documentation.

Enables semantic search over documentation stored in a Vectorize database using the Cloudflare Agents SDK, allowing users to query indexed content via natural language.

An unofficial MCP server that provides semantic search capabilities for Hugging Face models and datasets, enabling Claude and other MCP-compatible clients to search, discover, and explore the Hugging Face ecosystem using natural language queries.

A Python MCP server that enables semantic search through Search Labs blog posts indexed in Elasticsearch, allowing Claude to intelligently retrieve relevant information from the blog content.

A semantic search system for Claude Code transcript history, exposed as an MCP server so Claude Code can query its own past sessions.

Enables natural-language semantic search of a project's code through a local vector index, so questions that plain grep cannot match return ranked file:line references. It also indexes or refreshes projects on demand, reports index status, and optionally flags near-identical code across files.

Enables semantic code search across local projects and Git repositories using AI embeddings with ChromaDB. Supports both OpenAI and local Ollama models for private, enterprise-ready code analysis and similar code discovery.

Enables local MCP clients to search Obsidian Markdown vaults using semantic, keyword, and hybrid search, with local Ollama embeddings and storage in either Qdrant or PostgreSQL + pgvector.

Semantic, on-demand skill retrieval for Claude Code that saves tokens and improves skill discovery by replacing the native skill listing with vector embedding search.

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.

Enables coding agents to persistently store memories, artifacts, and context discarded by compaction into a searchable store, then automatically recall and inject the items relevant to the current task. It fuses BM25 keyword search with embedding-vector retrieval so differently-phrased memories still match, running as a fail-open sidecar that never blocks the agent session.

Enables semantic search through 1C codebase exports using local CPU-based RAG with sentence transformers and FAISS indexing. Supports fast XML file indexing and retrieval of 1C code with metadata parsing.

An MCP server for semantic code search using Qdrant vector database, enabling natural language queries to find relevant code snippets across indexed codebases.

MCP server for Turkish legal semantic search using NVIDIA nv-embed-v1 embeddings, with rate-limit resilience and query embedding caching for fast RAG responses.

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

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.