Model Context Protocol Servers

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.

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.

kubectl-ai is a natural language cluster operator and AI command assistant that translates plain-text prompts into executable Kubernetes commands. It serves as an interface between large language models and the Kubernetes API to enable cluster management through conversational text. The project implements a Model Context Protocol server to expose cluster operations as standardized tools for external AI clients. It uses a provider-agnostic model interface to support both cloud-based and local AI backends. The system covers natural language infrastructure control and AI-assisted DevOps through dynamic command translation and a bridge to the standard command line interface. It extends operational capabilities via plugin-based tool execution and integration with external Model Context Protocol servers.

mcp-use is a development framework designed for building, deploying, and managing servers, clients, and autonomous agents using the Model Context Protocol. It provides a comprehensive toolkit for creating servers that expose custom tools, data resources, and prompts to compatible AI agents. The project distinguishes itself by offering a complete lifecycle for protocol-based applications, including a dedicated hosting platform for production servers and a compliance validator to ensure servers meet marketplace publishing requirements. It also features an observability suite for tracing protocol traffic and a set of tools for generating the assets and metadata required for app store submissions. The framework covers broad capability areas including automated deployment pipelines with branch preview provisioning, comprehensive monitoring with latency and reliability analytics, and security via multi-tenant hosting and tool-level access control. It also includes UI integration for embedding customizable chat interfaces and widgets directly into applications. Development is supported through software development kits for TypeScript and Python, along with a command-line interface for project scaffolding and server boilerplate generation.

container-use is a containerized AI execution environment and code sandbox designed to provide a secure space for AI coding agents to execute commands and build applications. It functions as a workspace orchestrator that provisions isolated containers mapped to git branches, allowing multiple agents to operate in parallel without state conflicts or affecting the host system. The project serves as a Model Context Protocol server, bridging AI agents to containerized environments for standardized tool access. It enables a workflow for reviewing and merging changes made by agents within these isolated environments back into a local repository. The system includes capabilities for agentic workflow monitoring through command history logging and provides mechanisms for human intervention via direct terminal tunneling into active sessions. It further supports bidirectional file system syncing to facilitate the review and integration of agent-generated code.

MemMachine is a centralized memory management server and model-agnostic memory layer for large language models. It functions as a persistence layer that stores user profiles and conversational context, providing a decoupled data store that prevents vendor lock-in by serving different AI models through a consistent API. The system implements the Model Context Protocol to share persistent agent memories and session data with compatible AI clients. It utilizes a multi-tiered memory hierarchy, combining a graph-based conversation store for episodic interactions with a vector knowledge base for searchable long-term memory. The platform covers state management for AI agents, including the creation of individual user profiles and the maintenance of short-term working memory. It provides capabilities for natural language memory search, interaction recall, and profile-based data partitioning to ensure personalized AI behavior across multiple sessions. Connectivity is provided through a REST API gateway and language-specific SDKs to integrate the memory layer with external agent frameworks and AI models.

DesktopCommanderMCP is a Model Context Protocol (MCP) server that gives AI agents direct access to local files, shell commands, and system processes through natural language instructions. It acts as a unified bridge between conversational commands and desktop operations, enabling an AI to translate plain English into file management, code editing, system command execution, data analysis, and software scaffolding tasks without needing its own API. The server exposes these capabilities as structured tools via the MCP protocol, so any compatible agent can interact with the local environment in a controlled, predictable manner. What distinguishes this server is the breadth of integrated capabilities bundled into a single protocol interface. It functions simultaneously as a natural language file manager, a code scaffolding and editing platform, a data analysis and reporting engine, and a command execution and process management tool. Users can organize, convert, search, and edit files; generate or modify code; query spreadsheets and data files; run shell commands and manage long-running processes; and even deploy applications or provision cloud services—all through conversational requests. The server also supports remote command execution, server behavior configuration, and a containerized execution environment that isolates sensitive operations from the host system. Beyond these core functions, DesktopCommanderMCP can perform codebase structure analysis, automated documentation generation, code quality reporting, and multi-step workflow orchestration across external services. It integrates with third-party APIs, databases, and analytics tools without requiring manual configuration, and can chain actions across multiple services in a single conversation. The server is configurable with respect to blocked commands, allowed directories, and shell choices, and supports audit logging without restarting.

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.

This project is a curated library of community-driven prompt templates and personas designed to improve interactions with large language models. It functions as a prompt engineering guide, providing interactive tutorials and examples to teach advanced design and reasoning techniques. The library can operate as a Model Context Protocol server, providing a standardized interface for AI tools and agents to access prompt data as a service. For organizations, it offers a self-hosted repository option that allows for private deployment on internal infrastructure with custom authentication and data privacy. The system supports collaborative prompt management, enabling users to discover, share, and synchronize prompt templates within a shared dataset. It includes capabilities for content taxonomy and UI customization through a configurable theme system.

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.

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.

This platform serves as a centralized management system for organizing, refining, and versioning AI instructions and agent skills. It functions as a repository that enables users to store, categorize, and retrieve structured prompts, ensuring consistent performance across various artificial intelligence models. By integrating with the Model Context Protocol, the system allows external AI assistants and development environments to discover and access these instruction libraries directly. The platform distinguishes itself through its focus on prompt engineering and automated refinement, utilizing generative analysis to transform basic user instructions into structured, high-performance prompts. It supports multi-tenant white-labeling, allowing for isolated, custom-branded deployments that include secure identity management and granular access control. Additionally, the system incorporates an interactive educational environment designed to teach users effective techniques for constructing and optimizing AI interactions. Beyond core management, the platform provides semantic search indexing to facilitate efficient discovery of relevant instructions based on user intent. It also supports the development of complex agent skills and includes automated workflows that enforce behavioral standards for AI interactions. The system is designed for both individual use and enterprise-grade infrastructure deployment, offering tools for visual customization and interface localization to meet diverse organizational requirements.

This is a Model Context Protocol server that exposes Windows desktop automation and system administration functions to large language models. It provides programmatic control of mouse, keyboard, windows, and UI elements on Windows through simulated user input, while also enabling LLMs to manage the Windows registry, processes, files, and execute PowerShell commands through a remote interface. The server supports multiple transport protocols including stdio, SSE, and streamable HTTP, allowing flexible integration with different language model clients. It implements OAuth 2.0 with PKCE for secure remote access, along with bearer token authentication, TLS encryption, IP address restrictions, and SSRF protection to control access to Windows system functions. The server uses a TOML configuration system for storing settings, tool whitelists, and security policies. Desktop automation capabilities include screenshot capture with configurable resolution scaling and flash suppression, keyboard and mouse input simulation, application launch and window management, UI element interaction and state retrieval, and DOM mode browser automation. System administration features cover file and directory management, Windows Registry access, clipboard read and write, process listing and termination, and PowerShell or system command execution. The server can be installed as a background or login task that starts automatically at system startup.

Figma-Context-MCP is a design-to-code automation tool that functions as a server for the Model Context Protocol. It acts as a bridge between visual design platforms and development environments, enabling large language models to access design file metadata and component properties directly. The project distinguishes itself by providing a standard-compliant interface that translates design specifications into structured data. By extracting layout and styling information, it facilitates the programmatic conversion of design tokens and component requirements into actionable code structures. This tool supports automated frontend development by providing context-aware data to AI agents, ensuring that generated interfaces align with original visual intent. It covers the full design handoff process, from the retrieval of design metadata to the implementation of consistent design systems across a codebase.

This project provides a translation layer and set of adapters designed to bridge AI agents with the Model Context Protocol. It functions as an integration layer that allows agents to operate as protocol-compliant servers and enables the conversion of protocol-based tools into formats compatible with agent frameworks and logic graphs. The adapters facilitate tool interoperability by wrapping external protocol tools for use within agent workflows and exposing internal agent capabilities to any client implementing the Model Context Protocol. This creates a communication bridge that supports inter-agent discovery and coordination through a standardized protocol. Beyond tool translation, the project covers the orchestration of agentic workflows, including stateful thread management, asynchronous execution, and real-time event streaming. It also incorporates support for OAuth 2.0 authentication, durable execution checkpointing, and the deployment of agents via Docker containers.

Agent Zero is an autonomous AI agent framework designed to execute complex, multi-step workflows by managing its own environment, persistent memory, and external tool interactions. It functions as a Python-based automation library that enables agents to write code, execute terminal commands, and perform system-level tasks independently. The system is built to handle large-scale operations through hierarchical agent delegation, allowing for the coordination of subordinate agents to maintain focus and context. The platform distinguishes itself through a focus on secure, isolated execution and standardized integration. It utilizes a sandboxed environment for all system-level operations and incorporates a security-first approach to plugin management, automatically scanning external tools for vulnerabilities before deployment. By leveraging the Model Context Protocol, the framework provides a unified interface for connecting to external data sources and third-party tools, ensuring that agents can expand their functional capabilities while maintaining strict environment-based configuration isolation. The system supports a broad range of operational requirements, including persistent knowledge management, automated scheduling of recurring tasks, and secure credential handling. It provides tools for analyzing complex data and performing automated security assessments, ensuring that long-running tasks remain consistent and transparent. The framework is designed for developers to build and manage self-directed agents that operate within defined security boundaries.

This project is a UI component library and web layout framework providing pre-made interface elements and blocks for production-ready web applications. It functions as an AI design system that combines a collection of reusable components with a Model Context Protocol server to enable AI coding agents to discover and install interface elements. The system distinguishes itself through AI-driven automation, using the Model Context Protocol and schema-driven configurations to integrate design rules and installation commands directly into AI agents. This allows for the programmatic implementation of user interface components through an AI-integrated workflow. The framework covers a range of capabilities including a command-line interface for layout block deployment and a registry-based distribution system for managing assets. It specifically provides specialized layout blocks and sections tailored for ecommerce flows and marketing pages.

PraisonAI is an autonomous AI agent platform that coordinates multiple LLM-powered agents for research, planning, and execution of complex workflows. It functions as a multi-agent orchestration framework, a workflow builder, and a Model Context Protocol server, while also providing retrieval-augmented generation through vector knowledge bases. Agents can interact via CLI, web, or standardized protocols with sandboxed code execution. The platform distinguishes itself with a rich set of agent communication protocols, including A2A, REST, WebSocket, voice and telephony integration, and MCP, allowing agents to be exposed as services and connect to external systems. Comprehensive safety governance enforces human-in-the-loop approval for destructive actions, sandboxed code execution, policy-based tool permissions, and output validation. Memory and state management are advanced, with persistent memory across sessions, checkpoints, per-user isolation, and support for multiple backends including SQLite, PostgreSQL, Redis, MongoDB, Weaviate, and vector stores. Multi-agent orchestration includes planning, delegation, sequential and parallel execution, conditional branching, and compensation patterns for handling partial failures. Broader capabilities cover agent monitoring with cost tracking, telemetry, and live visualization, as well as testing and evaluation tools for debugging, replay, and batch assessment. Extensibility is provided through custom tools, MCP server connections, and a recipe management system for reusable workflows. Content processing includes image analysis and generation, OCR, speech synthesis and transcription, video analysis, and data analysis. Deployment options span REST APIs, messaging platforms, Docker and Kubernetes, and background job execution. Search and knowledge retrieval incorporate hybrid search, query rewriting, deep research, and web research with citations. Agents and workflows are defined in YAML and orchestrated through a command-line interface that also supports interactive coding, real-time chat, and voice interactions.

Higress is an AI API gateway and cloud-native traffic manager that functions as a Kubernetes ingress controller. It provides a centralized system for routing, securing, and optimizing traffic directed toward large language models, AI agents, and microservice architectures. The project distinguishes itself through deep AI orchestration, including the ability to host and manage Model Context Protocol servers that transform REST APIs into tools for AI agents. It features specialized AI infrastructure for model request proxying, protocol translation across multiple providers, and semantic-based caching to reduce token consumption and latency. Broad capabilities cover API lifecycle management and traffic control, including canary releases, load balancing, and rate limiting. The system includes a comprehensive security suite with WAF filtering, OIDC and OAuth2 identity integration, and automated TLS certificate management. Extensibility is provided via a WebAssembly-based plugin system that allows for hot-loading custom logic without interrupting traffic. The gateway can be deployed to Kubernetes or Docker and supports the Kubernetes Gateway API and Ingress standards.