"ts-node" 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.

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.

Enables AI agents to query a local knowledge graph built from document collections using hybrid search (BM25 + vector fusion) and entity-relationship extraction. Supports privacy-first, offline operation with tools for semantic search, entity graph exploration, and corpus statistics.

Enables making REST API calls to Teradata cloud services including Elastic Compute, Vector Store, OMS, and QueryGrid. Supports custom authentication, Socks5 proxy, and multipart file uploads.

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 building and querying vector-based knowledge graphs with node and edge management and semantic search.

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.

MCP server providing context usage estimation, conversation compaction, and durable semantic memory via local embeddings and SQLite.

Enables AI assistants to interact with Meilisearch through a standardized interface, supporting index and document management, search capabilities, settings configuration, task monitoring, and experimental vector search.

A Model Context Protocol server for generating text embeddings using OpenAI, Anthropic, or Ollama, with tools for single and batch embedding operations.

Enables semantic code search across multi-language codebases using natural language queries, integrated with Qdrant vector database for fast, cached retrieval.

A cloud-based vector memory service that provides AI assistants with persistent storage, semantic search, and entity management via the Model Context Protocol. It features multi-tenant isolation and bidirectional synchronization with macOS and Google contacts and calendars.

A TypeScript implementation of the Model Context Protocol server that enables creation, management, and semantic search of memory streams with Mem0 integration.

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.

Persistent, searchable archive and memory for Claude and ChatGPT. Native macOS MCP server, install from the App Store, no setup. Private, local and open source.

An MCP server designed to assist with generating, converting, and translating Milvus SDK code by retrieving relevant documentation and snippets. It supports PyMilvus code generation, ORM-to-client conversion, and cross-language translation between Python, Java, Go, and other supported languages.

MCP server for a self-hosted RAG system that enables AI tools to search and retrieve grounded answers from locally ingested documents via MCP tools, with local embeddings and no API key required.

Local-first semantic memory server with project indexing for AI assistants. It enables AI assistants to store, retrieve, and search memories and project code using embeddings and vector search.

Enables Claude Desktop to perform local-first semantic search, ingest documents, and manage a private knowledge base with hybrid search, PII redaction, and multi-format support.

Enables AI agents to interact with TigerGraph databases through the Model Context Protocol, supporting graph operations, schema queries, and GSQL execution via natural language.