Collapsing hundreds of API endpoints into two MCP tools
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Hundreds of endpoints distilled into two intelligence tools. 🛠️ At WRITER, we’re building an MCP gateway to empower our customers to connect hundreds of services and thousands of tools in a maximally secure, transparent manner. The problem? When it comes to security and scalability – MCP leaves a lot to the end user to figure out. Our own Dennis Thompson 🍉 broke down some of the key insights our engineering team is uncovering while building out our MCP implementation. He shares some of the initial challenges we faced – like context rot and tool calling accuracy – and how we distilled hundreds of API endpoints down to just two intelligent tools. Check it out: https://t.co/Eh3Qa6fYZI
Context
WRITER's gateway design aims to give an two entry points: search for an appropriate tool, then execute it. The engineering article describes converting API documentation into tool definitions and refining their descriptions. A search component compares an agent's request with those descriptions and returns ranked matches. The underlying endpoints remain available, but the agent need not consider all of them at once. The October 2025 article presents the two-tool interface as a development goal, while describing the ingestion and retrieval machinery behind it.
- MCP — The Model Context Protocol — an open standard that lets any AI assistant plug into any tool or data source without custom integration code.
- AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
- distillation — Training a small, cheap model to imitate a big one's outputs, keeping much of the capability at a fraction of the cost.
- context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
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