Python

Introduction I’ve been thinking about MCP scaling problems since I built and deployed an MCP server for the OpenDota API. In that post I talked about the pitfalls of auto-converting a REST API into MCP tools: context pollution, overlapping tool descriptions, the agent struggling to pick the right tool out of 200+ options. At the time, I framed those as reasons to curate your tool set carefully. But I didn’t have a good answer for what you do when you genuinely need a lot of tools. ...

Introduction You’ve probably heard about the Model Context Protocol (MCP) by now: an emerging standard for connecting AI agents to external data sources and tools. I wanted to explore building a serverless HTTP-based MCP server using the typical APIGW + Lambda pattern that is common in serverless applications, i.e. the way some MCP server provider might want to host MCP servers for public consumption. When thinking about which tools the MCP server should have, I wondered how hard it would be to convert an existing REST API into an MCP server - especially given some standard specifications like OpenAPI. ...