Repository History
5 repositories tagged with model-context-protocol
dcc-mcp-blender: AI-Driven 3D Workflows with an Embedded MCP Server
dcc-mcp-blender is a powerful Blender addon that integrates an embedded Streamable HTTP MCP server directly into Blender. This allows any MCP-compatible AI client to seamlessly control and automate your 3D modeling, animation, and rendering workflows. It offers over 200 pre-built tools and an extensible skill system for robust production environments.
MetaMCP: Unifying Model Context Protocol Servers with an All-in-One Gateway
MetaMCP is a powerful, self-hosted solution that acts as an aggregator, orchestrator, middleware, and gateway for Model Context Protocol (MCP) servers. It allows developers to dynamically combine multiple MCP servers into a single, unified endpoint, enhancing tool management and agent development. This TypeScript-based project simplifies the deployment and management of AI tools within a Dockerized environment.
FastMCP: The Pythonic Framework for Model Context Protocol Applications
FastMCP is a robust, Pythonic framework developed by PrefectHQ, designed to simplify the creation of Model Context Protocol (MCP) servers and clients. It provides a comprehensive application framework for connecting Large Language Models (LLMs) to tools and data, handling complexities like schema generation, validation, and protocol lifecycle. As the standard framework for MCP, FastMCP empowers developers to build powerful LLM-integrated applications efficiently.
Inspector: The Ultimate Tool for Developing MCP and ChatGPT Apps
MCPJam Inspector is a powerful local development client designed for testing and debugging MCP servers, ChatGPT apps, and MCP ext-apps. It provides a comprehensive suite of tools, including a widget emulator, OAuth debugger, and LLM playground, enabling developers to rapidly iterate on their projects without needing external services like ngrok or a ChatGPT subscription.
mcp-agent: Build Effective AI Agents with Model Context Protocol in Python
mcp-agent is a powerful Python framework designed to help developers build effective AI agents using the Model Context Protocol (MCP) and simple, composable workflow patterns. It fully implements MCP, providing robust support for agent lifecycle management and integrating patterns from Anthropic's 'Building Effective Agents'. This framework simplifies the creation of durable, production-ready agent applications.