Writing
Proof of Concept: Coding an MCP Client with Claude
This proof of concept was less about NBA data than about watching an AI coding agent work against a real integration boundary. The interesting part was not that Claude could write code. The interesting part was how much of the work became steering, scoping, verifying, and correcting the path when the system drifted.
The core idea
MCP changes the shape of AI development because it gives assistants a clean way to reach tools and data. Once an agent can inspect an API, call a local server, and iterate on failures, it starts to feel less like autocomplete and more like a junior implementation loop with very high throughput.
Why it matters
The POC also showed the limitation. The agent could move fast, but it still needed a human to define the right outcome, notice when the implementation was becoming overfit, and decide which errors mattered. The leverage came from the combination: AI for fast execution, human judgment for direction and acceptance.
How to use it
- Use concrete tasks with observable outputs when testing agentic coding workflows.
- Keep the human loop focused on requirements, failure interpretation, and acceptance criteria.
- Expect the best results when tools expose clear contracts instead of forcing the model to infer the world from prose.
What the POC really tested
The MCP client was less interesting as code generation and more interesting as an integration test for agentic work. It forced the assistant to discover an API shape, implement a protocol, handle credentials or configuration, expose a stable tool contract, and produce something another system could call. Those are the same failure points that show up in larger agent platforms.
The lesson is that AI coding looks magical when the acceptance test is loose and brittle when the acceptance test is real. The human has to provide the target contract, the verification path, and the taste for what should be simplified. The model can move fast inside that frame.
Better POC design
- Define the external tool contract before asking the agent to implement internals.
- Give the agent a small set of real tests, not only prose requirements.
- Record every human correction as a reusable instruction or eval case.
- Separate working demo from production readiness: auth, rate limits, logging, and error handling are separate gates.
Bottom line
The lesson was simple: MCP is not magic, but it gives agents the handles they need to become useful builders instead of isolated chat interfaces.