Implement ACP checkout as an MCP server, exposing checkout operations as MCP tools. Use when building an MCP-based commerce server for AI agents that use tool-calling to complete purchases.
Scanned 5/29/2026
Install to Claude Code
npx -y skills add OrcaQubits/agentic-commerce-skills-plugins --skill acp-checkout-mcp --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: acp-checkout-mcp
description: >
Implement ACP checkout as an MCP server, exposing checkout operations as MCP
tools. Use when building an MCP-based commerce server for AI agents that use
tool-calling to complete purchases.
---
# ACP Checkout — MCP Binding
## Before writing code
**Fetch live docs**:
1. Web-search `acp agentic commerce protocol MCP server implementation` for MCP binding guidance
2. Fetch `https://developers.openai.com/commerce/specs/checkout/` for checkout operation semantics
3. Web-search `site:github.com agentic-commerce-protocol MCP` for any official MCP examples
4. Fetch MCP SDK docs: web-search `site:github.com modelcontextprotocol python-sdk` or `typescript-sdk` for current SDK
## Conceptual Architecture
### What MCP Binding Means
ACP's REST checkout operations can be exposed as **MCP tools** via an MCP server. This allows AI agents that use tool-calling (Claude, ChatGPT, Gemini) to invoke checkout operations directly as tools rather than making raw HTTP calls.
### Mapping REST to MCP Tools
Each REST checkout operation becomes an MCP tool:
| REST Operation | MCP Tool Name | Description |
|---------------|---------------|-------------|
| POST /checkout_sessions | `create_checkout_session` | Create a new checkout session with items |
| POST /checkout_sessions/{id} | `update_checkout_session` | Update session (items, address, fulfillment) |
| GET /checkout_sessions/{id} | `get_checkout_session` | Retrieve current session state |
| POST /checkout_sessions/{id}/complete | `complete_checkout_session` | Submit payment to finalize |
| POST /checkout_sessions/{id}/cancel | `cancel_checkout_session` | Cancel the session |
### Tool Input Schemas
Each MCP tool accepts JSON input matching the corresponding REST request body. The tool's `inputSchema` should be derived from the ACP OpenAPI spec's request schemas.
### Tool Output
Each tool returns the CheckoutSession object (or error) as JSON, matching the REST response body.
### MCP Server Architecture
```
AI Agent (Claude/ChatGPT)
↓ tool call (JSON-RPC)
MCP Server (your code)
↓ business logic
Checkout Service (same logic as REST)
↓ payment
PSP (Stripe)
```
The MCP server wraps the same business logic that the REST endpoints use. The checkout service layer should be shared between REST and MCP bindings.
### Key Considerations
- **Idempotency** — MCP doesn't have HTTP headers, so pass `idempotency_key` as a tool parameter
- **API versioning** — Include `api_version` as a tool parameter or server configuration
- **Authentication** — MCP transport handles auth (stdio for local, SSE/streamable-HTTP for remote)
- **Error handling** — Return ACP error objects as tool errors with the same `type`/`code`/`message` structure
- **Statelessness** — Each tool call should be stateless; session state lives in the CheckoutSession object
### Use Cases
- AI agents that prefer tool-calling over raw HTTP
- Claude Desktop / Claude Code integrations
- Multi-agent architectures where commerce is one capability
- Rapid prototyping without building a full REST server
### Best Practices
- Share the checkout business logic layer between REST and MCP bindings
- Derive tool input schemas from the ACP OpenAPI spec (don't hand-write them)
- Include descriptive tool descriptions so the agent understands when to use each tool
- Test with the MCP Inspector before connecting to an agent
Fetch the latest ACP OpenAPI spec and MCP SDK documentation for exact schemas and server setup before implementing.
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