Best Figma MCP Servers

This is a Figma MCP server that exposes document manipulation capabilities to AI assistants through the Model Context Protocol. It functions as a bridge between AI tools and Figma, enabling programmatic creation, reading, updating, and deletion of design elements including frames, text nodes, components, and connectors. The server provides AI-powered design generation that translates natural language prompts into complete UI screens and design elements within Figma. It includes a design annotation system for adding, updating, and retrieving markdown-supported annotations on nodes, along with

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

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-

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. T

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

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. T

Jan is a local language model desktop application and AI assistant orchestrator. It provides a unified interface for interacting with both resident models and remote cloud AI providers. The project functions as a host for the Model Context Protocol, connecting AI models to external tools and data sources. It also operates as an OpenAI compatible API server, exposing local models through a standardized server endpoint for other applications to query. The system supports the creation of specialized AI personas with custom instructions and allows for the management of hybrid model environments,

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 protoco

The Open Agent Platform is an orchestration environment for building, deploying, and managing autonomous AI agents. It provides a framework for constructing both single-task performers and complex multi-agent systems, utilizing a central supervisor pattern to coordinate collaborative workflows and task delegation. The platform distinguishes itself through a graph-based execution model that defines the sequence of logic and tool calls, paired with a visual configuration interface that allows for the creation of agent workflows without manual coding. It incorporates enterprise-grade security by

This project is a comprehensive suite of AI tools and frameworks, featuring an LLM multi-agent orchestrator, an autonomous agent runtime, and a stateful application framework. It provides the infrastructure to build and manage specialized AI agents capable of coordinating complex tasks through graph-based workflows and shared state. The system is distinguished by its implementation of the Model Context Protocol, allowing for standardized resource discovery and communication between AI clients and servers. It further includes an AI-powered documentation generator designed to analyze source cod

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 in

The inspector is a diagnostic and validation tool for the Model Context Protocol. It provides an interactive interface and a transport proxy to discover, inspect, and execute the tools, prompts, and resources provided by an MCP server. The project serves as a debugger and compliance tester to verify that server implementations adhere to the protocol specification and JSON-RPC standards. It allows for real-time monitoring of message exchanges and logs between clients and servers across various transport layers, such as standard input/output and Server-Sent Events. The tool covers a broad rang