Baseten MCP

Baseten is an inference platform for running machine learning models in production. You package a model with its serving code, deploy it onto autoscaling GPU infrastructure, and call it over an HTTP endpoint. Alongside custom deployments it offers a catalog of ready to call open model APIs, multi step chains, and training. The Baseten MCP server exposes that control plane to AI coding agents. Rather than shelling out to the CLI or clicking through the dashboard, an agent connected to the server can see what is deployed across an account and whether each workload is healthy, cold, or scaled to zero; pull logs and stack traces from a live deployment to debug a failing request; promote a development deployment to production; and adjust autoscaling settings. A companion documentation endpoint answers platform questions from the official docs instead of from model memory, which cuts down on invented configuration options. This is aimed at teams whose deploy and debug loop already runs inside an AI coding assistant. The value is fewer context switches during an incident: the agent reads the error, fetches the matching logs, and proposes a fix without a human copying identifiers between terminal and browser. Access is authenticated with a Baseten key, and destructive operations carry tool annotations so an agent harness can require explicit confirmation before they run.