Building Scalable AI Tool Servers with Model Context Protocol (MCP) and Heroku
summary
Implements a MCP-compliant tool server in Python using FastAPI, uvicorn and the MCP Python package with pydantic schemas; demo deploys via Heroku’s button and buildpacks, scales by changing dyno count, and runs code in one-off dynos (demo installs numpy and executes a 50-number print). Exposes a list_tools schema over transports like SSE and stateless streamable HTTP. Deployment succeeds but demo responses are sometimes slow, and arbitrary-code execution requires strong isolation and auth.
AI-written summary · about
chapters (8)
- 0:00Introduction and context
- 2:07Understanding MCP
- 5:54The MCP request cycle
- 11:04Building Python tool servers
- 16:52Scalability considerations
- 19:14Deploying with Heroku
- 21:38Demonstrating tool deployment
- 34:44Security and advanced usage
related talks
30mComposable AI: Building Next-Gen AI Agents with MCP
26mBuilding Production-Ready MCP Servers: From Protocol to Patterns
28mMaking Rails AI-Native with the Model Context Protocol
25mTools in your AI's Toolbox : An introduction to MCP Servers
50mBeyond local tools: Dive into the Model Context Protocol (MCP)
33mThe Model Context Protocol: Connecting AI to Everything
34mPackaging & Deploying AI Tools with Model Context Protocol
32mMCP for dummies: Feed more context to LLM