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Expose MCP tools over streamable HTTP for FastAPI apps and AI assistants.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "MCP Streamable HTTP Server" yet — see the docs or source repo.
Use Python and FastAPI to build an MCP Streamable HTTP Server that provides a `get_weather` tool callable by AI assistants like Cursor. Include the project structure, dependency installation commands, complete example code, and local run steps.
A runnable FastAPI MCP service example with tool definitions, startup steps, and integration guidance.
I want to mount multiple MCP servers in a single FastAPI app, such as `calendar` and `database` toolsets. Design the route structure and provide sample code showing how to register and expose them separately.
Code and guidance for a multi-server mounting setup, clearly separating routes and registration for each toolset.
My AI assistant cannot connect to a FastAPI-based MCP Streamable HTTP Server. Give me a troubleshooting checklist covering ports, routes, streaming responses, CORS, logs, and deployment configuration, with common issue examples.
A systematic troubleshooting guide to identify connection failures and streaming communication issues.
Build and understand a simple MCP server with streamable HTTP transport.
Execute Python, bash, file operations, and text search over Streamable HTTP.
Example MCP Streamable HTTP client and server implementations in Python and TypeScript.
Demo tool for connecting LLMs to external tools and data sources via MCP.
Turn stdio task tools into a stateful HTTP MCP server.
Wrap a REST API as MCP tools for AI agent integration.