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Wrap FastAPI endpoints as MCP tools to learn LLM tool calling workflows.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "MCP Server Playground" yet — see the docs or source repo.
Using this MCP Server Playground, explain how to expose FastAPI endpoints as MCP tools and demonstrate the basic LLM calling flow for random facts and Japan FAQs.
A walkthrough of MCP tool wrapping, endpoint mapping, and an example flow from model invocation to returned results.
Give me a local run guide: how to start the FastAPI app, configure the LiteLLM proxy, and verify that the MCP tools can be called successfully by a model.
Step-by-step local setup and validation instructions, including launch commands, config essentials, and testing methods.
Based on the existing random facts and Japan FAQs tools, design a new MCP tool example and explain what new FastAPI routes and tool definitions are needed.
A new tool proposal, suggested API definitions, and key implementation points for MCP integration.
Turn a running FastAPI app into an MCP server for natural-language API calls.
Production-ready MCP server for query normalization, retrieval, and RAG prompt building.
Demo MCP server for calculations, time checks, notes, and code review prompts.
Deploy a production-ready MCP server with demo tools and interactive testing UI.
Call tools like weather lookup via MCP with reusable resources and prompts.
Convert OpenAPI specs into MCP servers so AI agents can call APIs.