Model Context Protocol Server Registry

Model Context Protocol is a standardized framework for connecting large language models to external data sources and executable tools. It enables the creation of a universal interface where servers expose tools, resources, and prompts that can be discovered and utilized by various AI clients. The protocol utilizes a JSON-RPC message system that is transport-agnostic, supporting both standard input/output for local processes and HTTP with server-sent events for remote connections. It emphasizes security and control by delegating model sampling to the client to keep API keys secure from servers and requiring explicit user approval for tool execution on local systems. The system covers broad capabilities including agentic workflow orchestration, URI-based resource mapping for filesystem and database access, and the delivery of interactive HTML-based user interfaces. It also includes comprehensive support for asynchronous task management, enterprise identity integration via OAuth and SSO, and a registry system for server discovery and versioning. The project provides client and server SDKs, alongside automated scaffolding tools for generating project structures and server boilerplate.

qmd is a local semantic search engine and RAG knowledge base indexer that functions as a Model Context Protocol server. It converts local documents, markdown files, and codebases into a searchable database to provide retrieval augmented generation capabilities for AI agents. The system exposes its search and retrieval tools via stdio or HTTP. It utilizes local model files for embeddings and reranking, supporting query expansion across multiple languages. The project employs abstract syntax tree based chunking to split source code at function and class boundaries. It implements hybrid vector-keyword indexing and metadata-driven context assignment to improve retrieval accuracy, while operating as a background daemon to maintain model residency in memory.

This project is a Model Context Protocol server that provides large language models with neural web search and webpage content extraction capabilities. It implements a standardized interface to expose research tools and resources to compatible clients. The server integrates a neural search engine to retrieve real-time internet data using semantic embeddings rather than keyword matching. It includes specialized utilities for company intelligence and reasoning-based deep research, enabling the collection and synthesis of organizational data and professional profiles. The system covers a broad range of research capabilities, including the discovery of technical code snippets and the application of advanced filters for domains and dates. It also features a content transformation pipeline that converts live webpages into clean markdown format. The project is developed in TypeScript.

This project is a tool for integrating existing HTTP APIs with AI agents by translating standard web endpoints into the Model Context Protocol. It provides a framework for constructing and managing libraries of functions that allow large language models to execute tasks and retrieve data. The system functions as an AI gateway that manages tool hosting, authentication, and routing. It includes capabilities for monetizing tool access through usage-based billing and payment processor integration, as well as the ability to publish service definitions to a gateway for commercial productization. The platform covers tool development and operational management, featuring semantic versioning for tool specifications to maintain compatibility. Performance is managed through rate-limiting middleware and distributed edge-based request caching.

Composio is an integration platform designed to connect autonomous agents with external software services and APIs. It functions as a tool orchestration framework and a middleware hub, providing a unified interface for managing the lifecycle, authentication, and execution of external tool definitions within agentic workflows. The platform distinguishes itself by utilizing the Model Context Protocol to standardize communication between artificial intelligence models and external data sources. It employs a provider-agnostic adapter pattern to decouple core logic from specific model providers and uses remote procedure call orchestration to route agent-generated function calls to external services through a centralized gateway. The system supports automated workflow orchestration, enabling the creation of complex task sequences across third-party business applications. It features dynamic tool discovery and session state management to maintain isolated execution environments, ensuring that agents have access to current service capabilities and authentication tokens during runtime. The project provides a software development kit that standardizes session creation and tool retrieval to facilitate integration within native development environments.

ddgs is a metasearch engine and web content extractor that provides a toolkit for programmatically retrieving search results from DuckDuckGo. It functions as a search API server and a Model Context Protocol server to integrate web search capabilities directly into large language model environments. The project distinguishes itself by aggregating text, image, news, and video results from multiple providers into a single interface. It includes a utility for fetching URLs and converting HTML content into markdown, plain text, or structured data. The system covers a broad range of search capabilities, including specialized searches for books and filtered searches for images and videos. It provides a command line interface for information retrieval, search data export to JSON and CSV, and the ability to define custom search provider extensions. The project can be deployed as a containerized service with integrated health checks and proxy support.

This project is a Model Context Protocol server that connects large language models to the Xiaohongshu social media platform. It acts as a connector and API wrapper, enabling language models to programmatically search, read, and publish media and text. The system provides automation for content discovery and publishing, allowing for the creation of image and video posts with associated titles and descriptions. It also facilitates social engagement by managing the posting of comments and tracking engagement metrics for specific entries. The tool covers data retrieval for user profiles, post details, and recommended content, as well as keyword-based search for finding relevant posts and media. It handles session authentication and the resolution of local file paths for media uploads.

git-mcp is a Model Context Protocol server that transforms Git repositories and static sites into structured context providers for AI assistants. It functions as a documentation retrieval tool and repository indexer, exposing codebases and project files as standardized tools to reduce hallucinations in large language model responses. The project converts raw repository files, READMEs, and external URLs into formats optimized for token consumption. It enables AI agents to perform query-based code searches and retrieve specific sections of project documentation to maintain up-to-date technical context. The system includes capabilities for dynamic documentation indexing, remote repository fetching, and an embedded web interface for in-browser documentation chat.

mcp-go is a Go implementation of the Model Context Protocol (MCP) providing an SDK and framework for building servers that connect large language model applications to external tools and data sources. It serves as a developer kit for implementing bidirectional communication and structured data exchange between AI clients and servers. The framework enables the creation of executable tools with structured output schemas, reusable prompt templates, and data resource exposure via URI templates. It supports multiple transport layers, including stdio, HTTP, and Server-Sent Events, using a transport-agnostic message bus to decouple protocol logic from the communication channel. The library includes capabilities for session-based context tracking, capability filtering, and handler middleware for logging and error recovery. It further provides observability through request tracing and lifecycle hooks, as well as security configurations for CORS and OAuth metadata exposure.

Magic MCP is a Model Context Protocol server and AI component generator that translates natural language descriptions into functional user interface code. It acts as an LLM design orchestrator, producing responsive web elements and layouts anchored on utility-first CSS styling patterns. The system features a side-by-side variation engine that generates multiple stylistic interpretations of a single prompt for comparative selection. It incorporates SVG-based asset integration for branding and iconography and utilizes template-based assembly to combine pre-defined style patterns with user-specified content. The tool covers a broad range of UI generation capabilities, including interactive widgets, data visualizations, complex dashboards, and responsive form layouts. It supports the creation of structural marketing sections, navigation headers, and animated components, providing real-time visual previews to validate designs during the synthesis process.

XcodeBuildMCP is a Model Context Protocol server and development tool bridge that provides AI agents with the ability to control xcodebuild, manage simulators, and automate the compilation and execution of Apple platform applications. It functions as a persistent daemon that proxies native IDE build and debug capabilities to external clients and agents. The project distinguishes itself by using the Model Context Protocol to expose build and device management tools through a standardized interface. It implements specialized skill priming and instruction configuration to ensure AI agents can interact with Apple development tools without needing to rediscover project conventions. The system covers a broad range of automation capabilities, including multi-platform project compilation, Swift package management, and the lifecycle control of iOS simulators. It supports physical hardware deployment via USB or Wi-Fi, remote debugging through LLDB command execution, and automated UI testing via gesture simulation and accessibility analysis. Observability is handled through real-time progress streaming using newline-delimited JSON, code coverage analysis, and the capture of device runtime logs.

Archestra is a platform for enterprise AI agent deployment and Model Context Protocol orchestration. It provides a centralized system for configuring specialized agents with specific system prompts and toolsets, and managing the deployment of Model Context Protocol servers that provide large language models with external tools and data sources. The system features an AI agent gateway that exposes configured agents as networked services for external clients and integrated development environments. It incorporates a security suite that provides deterministic guardrails to prevent prompt injection and data exfiltration, alongside application sandboxing and network restrictions for protocol servers. The platform includes a retrieval-augmented generation system with hybrid vector-text search for knowledge base management. It covers broader operational capabilities including role-based access control, enterprise identity integration, distributed telemetry export via OpenTelemetry, and operational cost management through budget limits. The infrastructure is designed for self-hosting within private networks using Kubernetes, with deployment supported via Helm charts and Terraform providers.

The Model Context Protocol C# SDK is a library for building clients and servers that implement the Model Context Protocol to integrate AI tools and resources. It provides an AI tool integration framework and a multi-modal content handler to exchange text, images, and binary resources between AI models and external context providers. The SDK utilizes a JSON-RPC communication library to manage bidirectional data exchange. It features a transport-agnostic communication layer that supports standard input and output, HTTP, and in-memory pipes, with specific integration for ASP.NET Core hosting. The framework covers tool and prompt management, including schema-driven tool generation and attribute-based capability discovery. It supports resource management via URI-template mapping, asynchronous task orchestration, and secure authorization workflows through token exchange and identity assertion.