Sample of FastAPI application implementing MCP Client
MCP.Pizza Chef: aswincandra
fast-mcp-client is an MCP client implemented using FastAPI, designed to orchestrate real-time context flow and tool interactions for LLMs. It enables developers to integrate and manage MCP protocol logic efficiently within FastAPI applications, facilitating modular and scalable AI workflows across various environments.
Use This MCP client To
Integrate MCP client functionality into FastAPI applications Manage real-time context flow for LLMs in web services Orchestrate tool calls and protocol logic in AI workflows Build scalable AI-enhanced web APIs with MCP support Enable modular AI agent orchestration in FastAPI environments
README
fast-mcp-client FAQ
How do I install fast-mcp-client in my FastAPI project?
You can install fast-mcp-client via pip and include it as a dependency in your FastAPI application to start using MCP client features.
Can fast-mcp-client handle multiple LLM providers?
Yes, fast-mcp-client is designed to be provider-agnostic, supporting OpenAI, Anthropic Claude, and Google Gemini models.
Does fast-mcp-client support asynchronous operations?
Yes, leveraging FastAPI's async capabilities, fast-mcp-client supports asynchronous context management and tool orchestration.
How do I configure fast-mcp-client for custom MCP servers?
Configuration is done via FastAPI settings or environment variables to specify MCP server endpoints and authentication.
Is fast-mcp-client suitable for production environments?
Yes, it is built on FastAPI, which is production-ready and scalable for real-time AI workflows.
Can I extend fast-mcp-client with custom tools or plugins?
Yes, fast-mcp-client supports extension by adding custom MCP tools and integrating additional protocol logic.
What logging or observability features does fast-mcp-client provide?
It integrates with FastAPI's logging and monitoring tools to provide observability into MCP interactions and errors.
Does fast-mcp-client support secure and scoped model interactions?
Yes, it follows MCP principles to ensure secure, scoped, and observable interactions with LLMs.