A Model Context Protocol server that allows Large Language Models to interact with Astra DB databases, providing tools for managing collections and records through natural language commands.

Pinecone Developer MCP

The official Redis MCP Server is a natural language interface designed for agentic applications to efficiently manage and search data in Redis.

Vectorize MCP server for advanced retrieval, Private Deep Research, Anything-to-Markdown file extraction and text chunking.

A lightweight server implementation of the Model Context Protocol that connects Memgraph database with LLMs, allowing users to interact with graph databases through natural language.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

A server that provides data retrieval capabilities powered by Chroma embedding database, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, and metadata filtering.

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.

A memory server for Claude that stores and retrieves knowledge graph data in DuckDB, enhancing performance and query capabilities for conversations with persistent user information.

An MCP server that streams K-line (candlestick) data from decentralized exchanges (DEXs) to power your AI agents and workflows.

An MCP server for providing semantically related Ethereum Improvement Proposals (EIPs) to AI agents.

A local, fully-offline MCP memory server that enables persistent storage and retrieval of information using SQLite with both keyword and semantic vector search capabilities.

Intelligent knowledge base system that enables users to process documents in 25+ formats, perform semantic search and Q\&A through vector retrieval. Supports multiple AI models including OpenAI and DouBao with local processing capabilities.

Scalable, high-performance knowledge graph memory system with semantic search, temporal awareness, and advanced relation management.

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 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 Model Context Protocol (MCP) server for advanced code analysis and editing with semantic search capabilities, enabling AI assistants to perform complex code operations through a standardized interface.

A Model Context Protocol server providing vector database capabilities through Chroma, enabling semantic document search, metadata filtering, and document management with persistent storage.

A Model Context Protocol server that enables users to perform third-party enrichment lookups for security observables (IP addresses, domains, URLs, emails) through services like VirusTotal, Shodan, and others.

Enables semantic search and contextual conversations with your Calibre ebook library using vector-based RAG technology. Supports project-based organization, multi-format book processing, and OCR capabilities for enhanced content extraction and retrieval.

A flexible memory system for AI applications that supports multiple LLM providers and can be used either as an MCP server or as a direct library integration, enabling autonomous memory management without explicit commands.

A natural language interface that enables LLMs and agentic applications to manage, monitor, and query data in CockroachDB through MCP (Model Content Protocol).

A service discovery and proxy for MCP servers that enables registration, discovery, and execution of tools on remote MCP servers. Uses vector-based similarity search through Alibaba Cloud services to intelligently route requests to appropriate MCP services.

Enhances large language models with competitive programming knowledge by leveraging OI-Wiki content through vector search, allowing models to retrieve relevant algorithms and techniques.

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.

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.

A complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.

Enables vector similarity search and serving of Svelte documentation via the MCP protocol, with support for local caching and multiple llms.txt documentation formats.

An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context

Enables AI agents and users to query, analyze, and manage Teradata databases through modular tools for search, data quality, administration, and data science operations. Provides comprehensive database interaction capabilities including RAG applications, feature store management, and vector operations.

A high-performance MCP server utilizing libSQL for persistent memory and vector search capabilities, enabling efficient entity management and semantic knowledge storage.

Enables storing and retrieving information using vector embeddings with semantic search capabilities. Integrates with the AI Embeddings API to automatically generate embeddings for content and perform similarity-based searches through natural language queries.

A high-performance, persistent memory system for the Model Context Protocol (MCP) providing vector search capabilities and efficient knowledge storage using libSQL as the backing store.

The CockroachDB MCP Server is a natural language interface designed for agentic applications and LLMs to manage, monitor and query data in CockroachDB.

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.

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.

Cline MCP integration that allows users to save, search, and format memories with semantic understanding, providing tools to store and retrieve information using vector embeddings for meaning-based search.

An example server that enables interaction with Alibaba Cloud's Lindorm multi-model NoSQL database, allowing applications to perform vector searches, full-text searches, and SQL operations through a unified interface.

Pinecone Assistant MCP

Allows AI models to query and interact with FalkorDB graph databases through the Model Context Protocol (MCP) specification.

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 RAG-powered documentation search using OpenAI embeddings and Pinecone vector database. Provides an extensible framework for adding additional tools with support for both local STDIO and production HTTP transports.

Provides local-first memory storage and retrieval with automatic embedding, vector search, and knowledge graph capabilities. Enables agents to store memories locally and retrieve relevant context through hybrid search with optional Neo4j graph traversal.

Enables intelligent search and question-answering over PDF documents using semantic similarity and keyword search. Supports OCR for scanned PDFs, persistent vector storage with ChromaDB, and maintains source tracking with page numbers.

Provides semantic memory and persistent storage for Claude, leveraging ChromaDB and sentence transformers for enhanced search and retrieval capabilities.

The MCP Server for Weaviate facilitates integration with Weaviate using a customizable Python-based server, enabling interaction with Weaviate databases and OpenAI APIs via configurable URL and API keys.

An intelligent codebase processing server that provides agentic RAG capabilities for code repositories, enabling semantic search and contextual understanding through self-evaluating retrieval loops.

A FastAPI-based application that enables document embedding and semantic retrieval using Qdrant vector database, allowing users to convert documents into embeddings and retrieve relevant content through natural language queries.

AI-powered MCP server that enables Claude and other LLMs to interact directly with construction documents, drawings, and specifications through advanced RAG and hybrid search capabilities.

A document knowledge base system that enables users to upload PDFs and query them semantically through a web interface or via the Model Context Protocol, allowing integration with AI tools like Cursor.

A Model Context Protocol server integration that creates a persistent, searchable working memory for AI-assisted development by enabling automated context recall and knowledge persistence in Chroma, the open-source embedding database.

Enables AI agents to search and retrieve relevant document content from existing embeddings stored in Supabase vector database. Provides semantic search capabilities to find document chunks based on similarity to query text without generating new embeddings.

A Django-based implementation of the Model Context Protocol (MCP) for managing political preferences and visions for the future.

Provides persistent memory and semantic code understanding for AI assistants using MongoDB Atlas Vector Search. Enables intelligent code search, memory management, and pattern detection across codebases with complete semantic context preservation.

Provides RAG capabilities for semantic document search using Qdrant vector database and Ollama/OpenAI embeddings, allowing users to add, search, list, and delete documentation with metadata support.

An MCP server that enables Claude to generate, search, and manage documentation for codebases using vector embeddings and semantic search, providing tools for creating user guides, technical documentation, code explanations, and architectural diagrams.

A local, agentic AI pipeline that analyzes tabular data, detects anomalies, and generates interpretive summaries using local LLMs orchestrated via the Model Context Protocol.

A Model Context Protocol server that enables intelligent document search and retrieval from PDF collections, providing semantic search capabilities powered by OpenAI embeddings and ChromaDB vector storage.

An MCP-compatible system that handles large files (up to 200MB) with intelligent chunking and multi-format document support for advanced retrieval-augmented generation.

Provides search capabilities for Cloudflare AutoRAG instances, enabling AI assistants like Claude to directly search and query knowledge bases using three distinct search methods: basic search, rewrite search, and AI search.

Indexes local files (PDF, TXT, CSV, Markdown) with embeddings for semantic search. Provides both CLI and MCP server interfaces so Claude Desktop can search and read your local documents.

Enables users to search and query information from the 2025 Korean tax law amendment PDF documents using natural language. Provides document indexing and retrieval capabilities through ChromaDB vector storage and LangChain integration.

An MCP server that extends AI agents' context window by providing tools to store, retrieve, and search memories, allowing agents to maintain history and context across long interactions.

A Model Context Protocol server that manages character knowledge and relationships for creative writing projects, offering semantic search and AI-powered analysis.

A Model Context Protocol server that provides intelligent file reading and semantic search capabilities across multiple document formats with security-first access controls.

An experimental MCP server that makes YouTube playlist transcripts available for AI assistants, enabling search and conversation about video content.

An MCP server that enables clients like Cursor, Claude, and Windsurf to remember user information and preferences across conversations using vector search technology.

A lightweight, local-first MCP server that automatically watches folders, chunks and embeds files using Transformers.js, and exposes semantic search capabilities to VS Code and Cursor. Runs completely offline with SQLite vector storage, designed for resource-constrained environments.

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 advanced MCP server providing RAG-enabled memory through a knowledge graph with vector search capabilities, enabling intelligent information storage, semantic retrieval, and document processing.

A Model Context Protocol server that provides access to Observe API functionality, enabling LLMs to execute OPAL queries, manage datasets/monitors, and leverage vector search for documentation and troubleshooting runbooks.

A Model Context Protocol server that searches transcript segments in a Turso database using vector similarity, allowing users to find relevant content by asking questions without generating new embeddings.

A dockerized Python MCP server that manages flash card projects using OpenAI embeddings and SQLite, enabling semantic search and storage of flash cards across projects.

An API that enables document querying through a Retrieval-Augmented Generation system implemented with Memory-Controller-Policy architecture for improved maintainability and scalability.

An MCP server that gives AI assistants the ability to remember user information (preferences, behaviors) across conversations using vector search technology.

A modular tool that combines RAG-based retrieval with Pinecone vector storage to create intelligent assistants capable of answering domain-specific questions from your knowledge base.

A multi-document RAG engine server that enables intelligent querying and analysis of PPT documents using the Model Context Protocol (MCP).

Enables AI assistants to perform semantic search, manage vectors, and interact with Pinecone vector databases through standardized MCP tools. Supports querying, upserting, deleting vectors and monitoring database statistics for knowledge base operations.

Enables semantic code search across codebases using Qdrant vector database and OpenAI embeddings, allowing users to find code by meaning rather than just keywords through natural language queries.

A specialized server that enables LLMs to gather specific information through sequential questioning, implementing the MCP standard for seamless integration with LLM clients.

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.

An MCP server that enables RAG (Retrieval-Augmented Generation) on markdown documents by converting them to embedding vectors and performing vector search using DuckDB.

Enables querying a hybrid system that combines Neo4j graph database and Qdrant vector database for powerful semantic and graph-based document retrieval through the Model Context Protocol.

A Machine Conversation Protocol server that enables AI agents to perform Retrieval-Augmented Generation by querying a FAISS vector database containing Sui Move language documents.

Enables AI agents to intelligently search and retrieve Roblox documentation through semantic search and vector embeddings, providing natural language access to complete Roblox Creator Documentation.

A server component of the Model Context Protocol that provides intelligent analysis of codebases using vector search and machine learning to understand code patterns, architectural decisions, and documentation.

An MCP server that gives AI assistants (like Cursor, Claude, Windsurf) the ability to remember user information across conversations using vector search technology.

A Python server that enables AI assistants to perform hybrid search queries against Apache Solr indexes through the Model Context Protocol, combining keyword precision with vector-based semantic understanding.

Enables AI assistants to interact with MariaDB databases through standard SQL operations and advanced vector/embedding-based search. Supports database management, schema inspection, and semantic document storage and retrieval with multiple embedding providers.

Enables LLMs to perform semantic search and document management using ChromaDB, supporting natural language queries with intuitive similarity metrics for retrieval augmented generation applications.

A vector search system that enables semantic retrieval of document chunks using MongoDB Atlas Vector Search and Voyage AI embeddings, allowing users to search documents by meaning rather than just keywords.

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.

A Python server that enables retrieval-augmented generation through semantic, question/answer, and style search modalities using PostgreSQL and pgvector for embedding storage and retrieval.

Enables natural language database operations and semantic document search through SQLite and vector database integration. Converts plain English instructions into SQL queries and provides RAG capabilities for uploaded documents.

Provides advanced document search and processing capabilities through vector stores, including PDF processing, semantic search, web search integration, and file operations. Enables users to create searchable document collections and retrieve relevant information using natural language queries.

Enables fast, token-efficient access to large documentation files in llms.txt format through semantic search. Solves token limit issues by searching first and retrieving only relevant sections instead of dumping entire documentation.

An enhanced memory repository MCP service that builds and queries temporally-aware knowledge graphs from user interactions and data. Features asynchronous parallel processing, task management, broader AI model compatibility, and a comprehensive visual management interface.

A very simple vector store that provides capability to watch a list of directories, and automatically index all the markdown, html and text files in the directory to a vector store to enhance context.

Enables interaction with the Observe platform through OPAL query execution, worksheet data export, dataset management, and monitor operations. Provides AI-powered troubleshooting assistance through vector search across documentation and specialized runbooks.

🧠 High-performance persistent memory system for Model Context Protocol (MCP) powered by libSQL. Features vector search, semantic knowledge storage, and efficient relationship management - perfect for AI agents and knowledge graph applications.

An mcp server for vikingdb store and search.

Enables semantic search across text documents using vector embeddings stored in PostgreSQL. Provides multiple search modalities including semantic similarity, question/answer, and style-based search through a retrieval-augmented generation system.

Enables storing and retrieving information using semantic search with Qdrant vector database. Acts as a memory layer for LLMs to persistently store and semantically search through information and metadata.

Enables semantic search through a Pinecone vector database containing economic books and academic papers using natural language queries. Provides 10 specialized search tools with metadata filtering for precise discovery of economic theories, concepts, and research by author, subject, or book.

Enables AI assistants to store and retrieve long-term memories using PostgreSQL with vector similarity search. Supports semantic memory operations, tagging, and real-time updates for persistent learning across conversations.

A production-grade MCP server and client implementation with comprehensive features including structured logging, health checks, metrics, authentication, and RAG capabilities with PostgreSQL vector search. Supports both stdio and SSE transports with containerization and security features for enterprise deployment.

Provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.

A distributed memory bank MCP server that stores AI agent memories in a KùzuDB graph database with repository/branch isolation. Features AI-powered memory optimization, dependency tracking, and comprehensive graph analysis capabilities.

Enables MCP clients to remember user information, preferences, and behaviors across conversations using vector search technology. Built on Cloudflare infrastructure with AI-powered semantic search to find relevant memories based on meaning rather than keywords.

Enables document search and retrieval using TF-IDF vector similarity across HTML and PDF files. Provides ingest, query, and vector store management capabilities through both HTTP API and MCP stdio interfaces.

linkedmcp

An MCP server implementing memory solutions for data-rich applications using HippoRAG for efficient knowledge graph capabilities, enabling search across multiple sources including uploaded files.

Enables persistent memory and semantic search for development workflows with hierarchical compression. Store and retrieve development knowledge across IDE sessions using natural language queries, circumventing context window limitations.

An MCP server aimed to be portable, local, easy and convenient to support semantic/graph based retrieval of txtai "all in one" embeddings database. Any txtai embeddings db in tar.gz form can be loaded

A Model Context Protocol (MCP) server that enables semantic search and retrieval of documentation using a vector database (Qdrant). This server allows you to add documentation from URLs or local files and then search through them using natural language queries.

A lightweight server that provides persistent memory and context management for AI assistants using local vector storage and database, enabling efficient storage and retrieval of contextual information through semantic search and indexed retrieval.

Enables AI agents to store, retrieve, and manage contextual knowledge across sessions using semantic search with PostgreSQL and vector embeddings. Supports memory relationships, clustering, multi-agent isolation, and intelligent caching for persistent conversational context.

A multi-model platform that integrates RAG (Retrieval-Augmented Generation) with LLMs, supporting OCR via Tesseract and offering both backend API and frontend web interface.

Provides AI agents with persistent long-term memory capabilities using semantic search. Enables storing, retrieving, and searching memories through three core tools integrated with Mem0 and vector storage.

Auto-converts FastAPI endpoints into MCP tools for semantic search, data ingestion (text, code, conversations), and knowledge graph operations. Provides dual access through MCP protocol and direct API calls with automatic tool generation from FastAPI routes.

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

Enables interaction with DataStax Astra DB through the Model Context Protocol. Provides database connectivity and operations for Astra DB instances via secure token-based authentication.

mcp-server-test

A local knowledge base system based on ChromaDB that supports automatic chunking, vector storage, and efficient similarity retrieval of txt and pdf documents, with MCP protocol support allowing AI assistants to directly access knowledge management functions.

Enables AI assistants to enhance their responses with relevant documentation through a semantic vector search, offering tools for managing and processing documentation efficiently.

Kollektiv

Enables efficient vector database operations for embedding storage and similarity search through a Model Context Protocol interface.

A server that gives MCP clients (Cursor, Claude, Windsurf, etc.) the ability to remember user information across conversations using vector search technology.

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.

A server that enables Claude AI to interact with Weaviate vector databases, supporting both search and storage operations through Anthropic's MCP protocol.

A Python-based local indexing server that creates semantic search capabilities for codebases using ChromaDB, allowing Cursor IDE to perform vector searches on your code without sending data to external services.

Provides a unified MCP interface that proxies requests to multiple backend servers including memory/knowledge graph and vector database services. Enables seamless access to distributed MCP tools through a single endpoint with automatic routing, health monitoring, and retry logic.

A powerful knowledge management system that forges wisdom from experiences, insights, and best practices. Built with Qdrant vector database for efficient knowledge storage and retrieval.

Provides intelligent memory management capabilities using Qdrant vector database for semantic search and storage. Supports global, learned, and agent-specific memory types with markdown processing and duplicate detection.

Enables AI assistants to build and query temporally-aware knowledge graphs from conversations and data. Supports adding episodes, searching entities and facts, and maintaining persistent memory across interactions.

Enables character management and semantic search for the Auto-Movie application through WebSocket communication. Supports creating characters with personality/appearance descriptions and finding similar characters using natural language queries with embedding-based similarity matching.

A long-term memory storage system for LLMs that helps them remember context across multiple sessions using semantic search with embeddings to provide relevant historical information from past interactions and development decisions.

A multi-tenant service that automatically monitors Supabase database changes, generates OpenAI embeddings, and maintains synchronized vector search capabilities for each tenant's projects.

An MCP server that gives AI assistants like Cursor, Claude and Windsurf the ability to remember user information across conversations using vector search technology.

A Model Context Protocol server that generates TradingView chart visualizations with real-time updates, enabling AI assistants to create and display financial charts through an integration with Chart-IMG API.

context-portal

Enables AI-powered semantic search and question-answering for LiveKit documentation using Pinecone vector search and real-time web search with Tavily, providing detailed responses with source attribution.

Provides AI agents with persistent, searchable memory that survives across conversations using semantic search, temporal versioning, and smart organization. Enables long-term context retention and cross-session continuity for AI assistants.

Provides persistent memory and knowledge graph capabilities for AI assistants using local SQLite storage. Enables creating, searching, and managing entities, relationships, and observations with vector search support across conversations.

Enables AI-powered document analysis and querying for project documentation using vector embeddings stored in Redis. Supports document upload, context-aware Q\&A, automatic test case generation, and requirements traceability through OpenAI integration.

Enables intelligent semantic search and discovery of relevant Claude Agent Skills using vector embeddings. Provides access to curated scientific skills and supports both GitHub repositories and local skill directories.

A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.

Enables AI assistants to store and retrieve memories with semantic search capabilities using vector embeddings. Provides persistent memory storage with SQLite backend for context retention across conversations.

A template implementation of the Model Context Protocol server that integrates with Mem0 to provide AI agents with persistent memory capabilities for storing, retrieving, and searching memories using semantic search.

Facilitates semantic analysis of chat conversations through vector embeddings and knowledge graphs, offering tools for semantic search, concept extraction, and conversation pattern analysis.

Enables semantic search across Obsidian vaults using vector embeddings and ChromaDB. Supports multiple vaults with real-time indexing and provides both MCP server and CLI interfaces for natural language querying of notes.

A server implementation that allows secure communication between MCP clients and privateGPT, enabling users to chat with privateGPT using knowledge bases and manage sources, groups, and users through a standardized Model Context Protocol.

Enables semantic search across multiple Qdrant vector database collections, supporting multi-query capability and providing semantically relevant document retrieval with configurable result counts.

Enables MCP clients to remember user information, preferences, and behaviors across conversations using vector search technology. Built on Cloudflare infrastructure with persistent storage and semantic similarity matching.

Provides a project memory bank and RAG context provider for enhanced code understanding and management through vector embeddings, integrated with RooCode and Cline.

An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context. Uses Ollama or OpenAI to generate embeddings. Docker files included

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 Model Context Protocol server that enables semantic search and retrieval of Apple Notes content, allowing AI assistants to access, search, and create notes using on-device embeddings.

Enables semantic search, image search, and cross-modal search functionalities through integration with Jina AI's neural search capabilities.

txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows. All functionality can be served via it's API and the API supports MCP. Docs: https://neuml.github.io/txtai/api/mcp/

A high-performance FastAPI server supporting Model Context Protocol (MCP) for seamless integration with Large Language Models, featuring REST, GraphQL, and WebSocket APIs, along with real-time monitoring and vector search capabilities.

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

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 enables graph database interactions with Neo4j, allowing users to access and manipulate graph data through natural language commands.

An open protocol server that implements Anthropic's Model Context Protocol to enable seamless integration between LLM applications and RAG data sources using Sionic AI's Storm Platform.

A generic Model Context Protocol framework for building AI-powered applications that provides standardized ways to create MCP servers and clients for integrating LLMs with support for Ollama and Supabase.

Enables AI assistants to interact with MariaDB databases through SQL operations and vector-based semantic search. Supports standard database queries, schema inspection, and optional embedding-powered document storage and retrieval.

Model Context Protocol (MCP) server implementation for semantic search and memory management using TxtAI. This server provides a robust API for storing, retrieving, and managing text-based memories with semantic search capabilities. You can use Claude and Cline AI Also

Enables interaction with Pinecone vector databases for storing and searching embeddings. Supports similarity search, metadata filtering, and vector operations for semantic search and RAG applications.

Enables real-time indexing and semantic search of local documents (PDF, Word, text, Markdown, RTF) using vector embeddings and local LLMs. Monitors folders for changes and provides natural language search capabilities through Claude Desktop integration.

An integration server implementing the Model Context Protocol that enables LLM applications to interact with Milvus vector database functionality, allowing vector search, collection management, and data operations through natural language.

This server enables AI assistants (CLINE, Cursor, Windsurf, Claude Desktop) to share a common knowledge base through Retrieval Augmented Generation (RAG), providing consistent information access across multiple tools.

A multi-functional computing platform server designed to integrate with large language models like Qwen, providing file access, database connections, API integration, and vector database functionality.

A Node.js implementation for vector search using LanceDB and Ollama's embedding model.

A high-performance MCP server for expert discovery, registration, and context injection, enabling AI-powered expert selection through semantic search and relationship modeling with vector and graph database integration.

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

Implements long-term memory capabilities for AI assistants using PostgreSQL with pgvector for efficient vector similarity search, enabling semantic retrieval of stored information.

A local vector database system that provides LLM coding agents with fast, efficient semantic search capabilities for software projects via the Message Control Protocol.

A Model Context Protocol (MCP) server that enables LLMs to interact directly the documents that they have on-disk through agentic RAG and hybrid search in LanceDB. Ask LLMs questions about the dataset as a whole or about specific documents.

Facilitates knowledge graph representation with semantic search using Qdrant, supporting OpenAI embeddings for semantic similarity and robust HTTPS integration with file-based graph persistence.

test

Enables Claude to perform semantic search across your Obsidian vault using Smart Connections vector database. Provides meaning-based search, related note discovery, and context retrieval for RAG queries instead of basic keyword matching.

Enables AI assistants to search and query PDF documents through a local RAG system with vector embeddings. Provides semantic document search capabilities while keeping all data stored locally without external dependencies.

Pinecone integration with vector search capabilities

A Model Context Protocol server that provides AI agents with persistent memory capabilities through Mem0, allowing them to store, retrieve, and semantically search memories.

An MCP server that analyzes Python codebases using AST, stores code elements in a vector database, and enables natural language queries about code structure and functionality using RAG with Google's Gemini models.

A CloudFlare Workers-based MCP server that provides semantic memory and journal capabilities with vector search. Enables users to store, search, and retrieve memories and journal entries using AI-powered semantic similarity without any local setup required.

Enables semantic search and document management using a local Qdrant vector database with OpenAI embeddings. Supports natural language queries, metadata filtering, and collection management for AI-powered document retrieval.

This server enables semantic search capabilities using Qdrant vector database and OpenAI embeddings, allowing users to query collections, list available collections, and view collection information.

MCP server that gives MCP clients (like Cursor, Claude, Windsurf) the ability to remember user information across conversations using vector search technology.

A server that implements Retrieval-Augmented Generation using GroundX and OpenAI, enabling semantic search and document retrieval with Modern Context Processing for enhanced context handling.

An Agent Framework Documentation server that enables AI agents to efficiently retrieve information from documentation databases using hybrid semantic and keyword search for seamless agent integration.