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"Guide to Creating Test Cases and Deploying in Jenkins" matching MCP servers:
- Alicense-quality-maintenanceProvides semantic search capabilities over the Plesk Extensions Guide documentation using Retrieval-Augmented Generation (RAG) and vector embeddings. It enables AI assistants to retrieve relevant technical information and answer natural language queries regarding Plesk extension development.
- AlicenseDqualityCmaintenanceA lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.12MIT
- Flicense-qualityDmaintenanceEnables semantic search and document retrieval from OpenAI Vector Store, allowing users to search documents using natural language queries and fetch complete document contents through ChatGPT.
- Flicense-quality-maintenanceA local Retrieval-Augmented Generation system that enables users to ingest markdown files into a FAISS-powered vector knowledge base for semantic search. It provides tools for document indexing and context retrieval to support informed LLM queries without external dependencies.
- AlicenseAqualityCmaintenanceEnhanced MCP knowledge graph memory server with cloud persistence and semantic search, acting as a drop-in replacement for the standard memory server.10835MIT
- FlicenseAqualityAmaintenanceMCP server for semantic search in an Obsidian Second Brain vault using self-hosted Qdrant and Google Gemini embeddings.3
- AlicenseAqualityCmaintenanceA 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.1410MIT

openserverless-mcpofficial
AlicenseAqualityBmaintenanceAn MCP server for OpenServerless that exposes action tools for creating, invoking, and managing API endpoints with integrated services like S3, PostgreSQL, Redis, and Milvus.81Apache 2.0- AlicenseAqualityDmaintenanceEnables AI agents to manage and search data in Redis using natural language. Supports hashes, lists, sets, sorted sets, streams, JSON, and vector search.44MIT

Redis MCP Serverofficial
AlicenseBqualityBmaintenanceThe official Redis MCP Server is a natural language interface designed for agentic applications to efficiently manage and search data in Redis.53563MIT- AlicenseBqualityDmaintenanceEnables Claude to store and query personal finance transactions using semantic search. Transactions are persisted in ChromaDB and JSON, allowing natural language questions about spending trends and portfolio allocations.4MIT
- AlicenseBqualityCmaintenanceA memory server for Claude that stores and retrieves knowledge graph data in DuckDB, enhancing performance and query capabilities for conversations with persistent user information.8659MIT
- AlicenseBqualityDmaintenanceAn MCP server for querying and managing LlamaIndex documents stored in Qdrant vector databases, with automatic embedding model detection and extensive tools for search, retrieval, and collection management.17Apache 2.0
- AlicenseCqualityDmaintenanceProvides 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.71414MIT

MCP MariaDB Serverofficial
Alicense-qualityCmaintenanceAn interface for managing and querying MariaDB databases that supports standard SQL operations alongside advanced vector and embedding-based search capabilities. It enables AI assistants to seamlessly integrate relational and vector data workflows through a standardized protocol.189MIT- Alicense-qualityBmaintenanceLocal-first MCP server for long-term memory of dev sessions. It indexes session metadata with embeddings in Postgres/pgvector, enables semantic search over past sessions, and resolves only relevant session files for context.21MIT
- Alicense-qualityCmaintenanceA 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.12MIT