One AI client writes the backend in the morning. A different assistant reviews the frontend in the afternoon. Every fresh conversation starts the same way: introduce the project, paste the interface definitions, explain the fields, and add the warning not to use last week's version.

The exhausting part is not a long prompt. It is that none of these introductions become a durable asset. Something made clear in one conversation is guessed at in the next.

As AI tools multiply, a project needs a source of context that lives outside the chat window. In Powerduck that position belongs to the local OpenAPI file, and MCP is how AI tools reach it.

Project knowledge should be queryable, not recited

API facts have a strict, queryable shape: which operations exist, what parameters they accept, what comes back, and what auth they require. Saved in a local specification, those facts can be reviewed and evolved like code. The AI conversation does the work; confirmed results return to the file.

The dev MCP server then exposes those facts to any MCP-capable coding client. Instead of swallowing the whole document, the agent searches for the relevant operations, reads the full contract for the ones it needs, and follows references into shared component schemas. Building the order detail page means reading exactly the operations and models that page touches, and the facts it retrieves become the basis for later tool calls and verification.

Handoff changes from "let me explain again" to "read the project"

A teammate picks up the refunds feature. They open the specification in Powerduck, start the dev MCP server, and point their own AI assistant at it:

Query the current refund endpoints and the related order models. List the fields, auth requirements, and error handling needed to implement the refund page, then propose an implementation plan.

That is more inspectable than copying explanations out of someone else's chat history. When two people disagree, they can point to a field and a version of the file instead of arguing over what an assistant said in some forgotten thread. When the specification is edited and saved, the dev MCP server serves the updated file on the next query — the team maintains the contract once, and every client fetches it on demand.

Local-first keeps the tool choice open

A plain local file has unglamorous but important powers. You can diff it, commit it alongside the code, and restore a previous version when an experiment goes wrong. Those powers matter precisely when AI is making broad changes: one generated proposal can touch several endpoints and schemas, and the team needs to see exactly what moved — and how to get back.

The file also keeps the core API definitions independent of any single conversation or vendor. Powerduck stays the place to design and verify the API, while any other MCP-capable development tool can participate.

One boundary is worth stating plainly: a local file and a model's data boundary are different things. When a specification or tool result is sent to a cloud model, that content enters the model call. Pick the model and the context scope according to your own requirements — we wrote about exactly what leaves the machine in our local-first data audit.

AI-native means the tools collaborate around the project

The division of labor is straightforward. The built-in assistant proposes contract changes; external coding agents read the contract and run verification through MCP; people check the business rules and save what is confirmed. Project context stops being a passive briefing and becomes an interface that tools can query and connect to verification.

Changing developers, models, or AI clients should not mean rebuilding the team's entire understanding of the API. Start with one habit: every contract decision that is confirmed and will be referenced again goes back into the local OAS. Conversations end. The project knowledge should stay.