"Understanding Perl, Language Server Protocol (LSP), and Abstract Syntax Trees (AST)" matching MCP servers:

Enables semantic search and interaction with Fuel Network documentation and Sway Language resources within AI-powered development environments. It provides high-quality technical context using a local vector database to assist with smart contract development and FuelVM queries.

A conversational application server that integrates LLM capabilities via Ollama with vector memory context, supporting multiple users, sessions, automatic history summarization, and a plugin system for executing real actions.

An MCP server that integrates with LangChain and ChromaDB to provide documentation search for AI libraries and vector database management.

MCP server for semantic code search with AST-aware chunking, hybrid vectors, and query syntax.

Provides semantic search capabilities by connecting Claude Desktop to a Cloudflare Workers backend powered by Vectorize. It enables natural language querying of knowledge bases using vector similarity and edge-based embedding generation.

A server that enables vector and keyword search capabilities in Typesense databases through the Model Context Protocol, providing tools for collection management, document operations, and search functionality.

mcp-server-test

Enables AI agents to interact with Milvus vector databases and Zilliz Cloud through natural language, allowing users to create clusters, manage collections, insert vector data, and perform semantic searches directly from their AI assistants.

An MCP server that provides semantic memory on top of Qdrant vector search, with tools to store and retrieve information using embeddings.

Enables AI agents to autonomously create and manage topic-specific vector knowledge bases with end-to-end functionality including project creation, content ingestion from URLs, semantic search, and progress tracking. Provides a complete research workflow without exposing low-level APIs.

A server that provides access to Baidu Cloud Vector Database functionality through the Model Context Protocol, enabling LLM applications to perform vector searches and database operations via natural language.

Provides AI assistants with vector search and persistent memory capabilities using ZeroDB. Enables storing and retrieving context across sessions, semantic document search, and analytics through natural language interactions.

A Model Context Protocol server implementation that enables LLMs to interact with NebulaGraph database for graph exploration, supporting schema understanding, queries, and graph algorithms.

An MCP server for storing and retrieving memories using Qdrant vector search, acting as a semantic memory layer.

A basic serverless MCP server using LanceDB to store and retrieve documents via three tools: ingest, retrieve, and get table details.

A Model Context Protocol server that enables semantic search capabilities by providing tools to manage Qdrant vector database collections, process and embed documents using various embedding services, and perform semantic searches across vector embeddings.

Enables fetching relevant content and embeddings from Supavec via the Model Context Protocol, allowing AI assistants like Claude to access vector search capabilities.

Enables AI-powered analysis of Ethereum blockchain data through semantic search, natural language queries, and structured filtering. Provides comprehensive access to addresses, transactions, blocks, tokens, and smart contracts with real-time blockchain intelligence.

Enables AI agents to interact with INFINI Easysearch (compatible with Elasticsearch/OpenSearch APIs) through 121 tools covering cluster management, index operations, document manipulation, search queries, snapshots, and monitoring.

Provides a private, on-device memory with semantic search for MCP clients, enabling encrypted storage and recall of context facts without external API calls.