What is the Model Context Protocol (MCP)?
MCP is a standardized way for LLM applications to securely access data and functionality. It is described as "the USB-C port for AI" and lets servers expose resources, tools, and prompts.
@Sobroinc
基本信息
FastMCP server containerized for deployment in Google Kubernetes Engine alongside enhanced-mcp-agent
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"fast-mcp": {
"command": "uv",
"args": [
"pip",
"install",
"fastmcp"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
FastMCP v2 is the standard Python framework for building and interacting with Model Context Protocol (MCP) servers and clients. It provides a high-level, Pythonic interface for exposing tools, resources, and prompts, and includes advanced features like client support, server composition, authentication, and automatic generation from OpenAPI specs.
Install with uv pip install fastmcp, then create a server instance with FastMCP("name") and decorate Python functions as tools, resources, or prompts. Run locally with fastmcp run server.py or mcp.run(). Use the Client class to connect to any MCP server programmatically via stdio, SSE, or in-memory transports.
MCP is a standardized way for LLM applications to securely access data and functionality. It is described as "the USB-C port for AI" and lets servers expose resources, tools, and prompts.
FastMCP 1.0 was incorporated into the official low-level Python SDK. FastMCP 2.0 is a complete toolkit that goes beyond the core protocol, adding client libraries, authentication, server composition, OpenAPI generation, testing tools, and production-ready infrastructure.
The Client supports Stdio, SSE, and In-Memory transports. It often auto-detects the correct transport from the connection string and also supports connecting to multiple servers through a unified
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