Input schema, output shape, API limits, and failure behavior are explicit.

Build workflows operators can run and explain
Practical paths for business owners becoming operators. Start in the OpenAI ecosystem with the Codex app, create a RapidAPI-backed MCP, then use Canon image rules to make boundaries, policy, proof, and handoff visible.
- Learning paths 2 / 11 lessons
- Working surface Codex app + MCP
- Evidence Canon workflow images
- Outcome Operator-ready proof
Prompt. Create. Prove.
Two operator paths create the workflow and make it visible; the third scene shows the artifacts that prove another operator can run and explain it.
Build Your First Business MCP
One practical path: use the Codex app to turn a RapidAPI business-data endpoint into a stable local MCP server for a real operator workflow.
- 01 · Prompt Start in the Codex app
Use the Codex app and its MCP-building skill to turn a concrete operator question into a narrow tool contract.
- 02 · Create Wrap the data source
Create the MCP server, connect one RapidAPI endpoint, and keep the schema inspectable.
- 03 · Prove Make the work visible
Use Canon image rules to show the object, boundary, policy gate, receipt, owner, and next action.
- 01 What Codex Uses MCP For10 min
Understand how Codex uses MCP tools and its MCP-building skill to make business workflows repeatable.
- 02 Scaffold an MCP Server20 min
Use the Codex MCP-building skill to plan a minimal TypeScript server, then scaffold it locally.
- 03 Add a Business Search Tool25 min
Connect RapidAPI Local Business Data with Zod input validation and machine-readable output.
- 04 Connect the Server to Codex15 min
Register the server in the Codex app, inspect the MCP settings, and invoke the tool from chat.
- 05 Test, Debug, Iterate25 min
Use Inspector, Codex config checks, and stderr logs to diagnose API, schema, and prompt failures.
- 06 Ship and Extend10 min
Document the tool contract, safety model, evidence loop, and next useful tools.
Make Your Workflow Visible
Use Canon image rules to turn an operator workflow into clear maps, MCP boundaries, policy gates, validation receipts, and handoff artifacts.
The course is judged by artifacts, not vibes.
The learning loop closes only when another operator can inspect the contract, run the server, read the workflow boundary, and name the next scoped extension.
Codex can find and run the server from your environment.
The object, MCP boundary, policy gate, owner, and receipt are visible.
You leave with a scoped extension instead of a vague automation roadmap.
- Tool contract
- Local config
- Workflow image
- Next workflow
Build the smallest useful workflow first.
Start with one Codex prompt, one endpoint, one schema, and one MCP call. Make the boundary visible with Canon, use .space to test it, return to .ltd for the governing principle, or carry the named workflow into .agency practice.
- Owner
- Learner / operator
- Authority
- Artifact review
- Proof
- Working MCP + workflow image
- State
- ready
- 01 .ltd Canon Clarify the principles, standards, and judgment that should guide the work.
- 02 .io Research Inspect the evidence, field reports, and operating notes behind the claim.
- 03 .learn School Follow a guided path and leave with an inspectable working artifact.
- 04 .space Workbench Run the tools and rehearse the behavior before the pattern becomes delivery.
- 05 .agency Build Map one workflow, qualify the control boundary, and choose the delivery path.