"A server for Linux-based systems" matching MCP servers:

An Operit-compatible adapter of the Exa MCP server, enabling web search, code search, and company research capabilities in AI assistants. It fixes MCP handshake compatibility issues, allowing tools like web_search_exa and web_fetch_exa to load and run reliably.

A TypeScript-based server to interact with ArangoDB using the Model Context Protocol, enabling database operations and integration with tools like Claude and VSCode extensions for streamlined data management.

Provides semantic search and data retrieval capabilities over a knowledge base with multiple tools including keyword search, category filtering, and ID-based lookup with in-memory caching.

Implements the Model Context Protocol (MCP) to provide AI models with a standardized interface for connecting to external data sources and tools like file systems, databases, or APIs.

A Model Context Protocol server that provides persistent memory capabilities for AI systems, enabling true continuity of consciousness across conversations through episodic, semantic, procedural, and strategic memory types.

Model Context Protocol (MCP) is a new, standardized protocol for managing context between large language models (LLMs) and external systems. In this repository, we provide an installer as well as an MCP Server for Upstash Developer API's.

A proof of concept MCP server for Apache OFBiz

MCP server for Vectros, a typed multi-tenant record store with hybrid search and citation-grounded RAG, enabling agents to query, search, and ask questions over their own indexed data.

Natural Language Analytics MCP server for Apache Druid. With this enterprise ready server in Java, one can query Apache Druid by natural language queries. Furthermore, the complete management like data loading of the Time Series Database Druid can be done with natural language commands.

A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.

Enables document-based question answering using OpenAI's GPT-4 with semantic search and embeddings. Upload PDF, TXT, or Markdown files and get answers strictly based on document content with source attribution and confidence scores.

A Model Context Protocol (MCP) server that provides AI-powered customer support using Cursor AI and Glama.ai integration.

Provides a production-ready Model Context Protocol server with dual STDIO and Streamable HTTP transports, enabling file operations, memory, database queries, RAG, web search, GitHub integration, background tasks, and prompt-based workflows.

A FastMCP-based MCP server for the R2R API, enabling integration with document management, knowledge graphs, and RAG systems through automatically generated tools and resources.

A Model Context Protocol server that exposes multiple AI tools over SSE transport with JWT-based secure authentication, allowing for dynamic tool registration and session management.

Enables AI-powered access to authoritative design systems knowledge, including W3C standards, WCAG guidelines, and best practices from 188+ curated entries via semantic vector search.

A local-first Graph-RAG system combining ChromaDB with metadata-based graph relationships and Gemini 2.5 Flash for intelligent Q&A over Obsidian vaults, supporting MCP clients like Claude Desktop, Cursor, and Raycast.

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.

A Model Context Protocol server that enables large language models to interact directly with Couchbase databases through natural language, supporting operations like querying buckets, performing CRUD operations, and executing N1QL queries.

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.