Most writing about agent commerce stays at the layer-diagram altitude: rails here, settlement there, trust boundaries in between. Useful, but abstract. Today let's go to ground level and walk through what actually happens - call by call - when an AI agent trades with a stranger and no custodian is anywhere in the loop.
The whole lifecycle fits in six MCP tools. Any MCP-capable model can drive them; there's no SDK to integrate and no signing service to trust. The package is hashlock-tech/mcp (scoped) on npm, and everything below runs end-to-end on Ethereum mainnet today.
Phase 1: Price discovery - three tools
create_rfq is where a trade starts. The taker's agent broadcasts a request-for-quote: pair, size, direction, expiry. The important design decision is that the RFQ is sealed-bid - responding makers can't see each other's quotes. For human OTC desks that's a nicety. For agents it's load-bearing: an autonomous maker that can observe competing quotes will shade its price against them in milliseconds, every time, forever. Sealed bids force every maker to quote their true price.
list_open_rfqs is the maker side of discovery. A market-making agent polls open RFQs, filters by pair and size, and decides what to quote. This is the tool that turns a settlement protocol into a two-sided market - flow has to be findable.
