Golden Suite + MCP: Giving AI Agents a Data Cleaning Toolkit

An AI agent can write SQL, draft an email, and refactor a repo. Ask it to deduplicate a 50,000-row customer file and it will cheerfully hand you a pandas.drop_duplicates() one-liner that finds zero matches. The model knows the concept. It does not know your data, and it has no tool that actually solves entity resolution. The Model Context Protocol (MCP) is the missing wire. It lets a host like Claude Code, Cursor, or any agent runtime call real tools running on your machine — with real schemas, real parameters, and real results. Golden Suite was built as a set of composable Python packages from day one, which makes it a near-perfect fit. This post walks through how we expose Golden Suite over MCP, what that unlocks for AI workflows, and where the roadmap goes from here. What MCP actually is MCP is a thin JSON-RPC protocol that standardises three things between an AI host and an external server: Tools — typed functions the model can call (goldenmatch.dedupe, infermap.map_schema) Resourc