Verify Local Mcp

Verify local MCP

Two layers, in order. The protocol layer proves the server and provider behave; the consumer layer proves an agent that has never seen the code can use the tools well. They catch different bugs — protocol testing found DataForSEO quirks (zoom-dependent empty SERPs), the consumer probe found ergonomics failures (9KB provider rows overflowing client token budgets, fractional inputs rejected upstream with raw provider errors). Do both.

1. Boot

• .env.local needs AUTH_MODE=local_noauth and DATAFORSEO_API_KEY (base64 of login:password). Never print the key.
• Start pnpm dev:agents in the background. The server URL is branch-prefixed: http://<branch-suffix>.open-seo.localhost:1355 (the exact URL is printed on boot; logs tee to .logs/dev-server.log).
• With local_noauth, /mcp needs no token. Vite hot-reloads server code, so fix → re-call without restarting.

2. Protocol smoke (cheap, deterministic)

Raw JSON-RPC against /mcp — the layer for asserting exact shapes and driving edge cases (resume taskIds, empty results, invalid inputs):

curl -sS http://<url>/mcp \
  -H 'content-type: application/json' -H 'accept: application/json, text/event-stream' \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'
# tools/call: {"method":"tools/call","params":{"name":"<tool>","arguments":{...}}}

• Bootstrap: list_projects, then create_project if empty — most tools need a projectId.
• Test the happy path AND at least one edge per changed tool: an empty result (obscure query), an invalid identifier, and for queued tools the full lifecycle including resuming with the returned taskId.
• These are real, billed DataForSEO calls (metering itself short-circuits in local_noauth — billing needs unit tests, not this). Keep depths 10–20.

3. Consumer probe (the ergonomics test)

Spawn a headless Claude subprocess connected as a real MCP client. Write a config:

{
  "mcpServers": {
    "openseo-local": { "type": "http", "url": "http://<url>/mcp" }
  }
}

Then run a NATURAL task — never name the tools; whether the model finds them from descriptions alone is the test:

claude -p "<natural task a customer would ask>. Keep spend minimal: depths 10-20, one 3x3 grid max, ~10 paid calls.
Deliver two sections: 1. FINDINGS — the task result. 2. MCP FEEDBACK — critique the MCP as a first-time consumer:
were descriptions enough to pick tools without trial and error? confusing schemas, surprising output shapes or sizes,
unclear errors, credit-cost surprises? Did async/taskId flows behave as described? List anything that made you hesitate or retry." \
  --mcp-config mcp-local.json --strict-mcp-config \
  --allowedTools "mcp__openseo-local,mcp__openseo-local__*" \
  --model sonnet --max-turns 30

Use --model sonnet as the typical-client proxy — if sonnet navigates it cold, weaker clients likely can too. Read FINDINGS for correctness (did it get real, sensible data?) and MCP FEEDBACK for the rubric below.

4. Ergonomics rubric — what feedback to act on

• Tool selection: the probe should pick the right tool first try. Retries or wrong-tool detours mean a description needs a sharper "use this when / not this" sentence.
• Schemas: every constraint the provider enforces silently must be in the field's .describe() (units, whole-number requirements, defaults, what's ignored when). If the probe guessed-and-retried an input, encode the rule server-side (coerce/round) or document it — prefer coercing.
• Output size: budget roughly a few KB per row. Provider rows carrying popular_times/attribute trees/photo URLs must be trimmed to the fields the tool's job needs; point to the single-entity tool for the full shape.
• Errors: actionable, never a raw upstream field name without a hint at the fix. Failures after a billed step must keep the recovery handle (e.g. the taskId) in the message.
• Async copy: descriptions must match typical latency ("usually completes within this call") and the resume path must actually work when driven by the probe, not just by curl.
• Credit honesty: each description's credit sentence matches reality, including cache-hit and resume paths.

5. Iterate and clean up

Fix findings → hot-reload picks them up → re-verify just the changed behavior via curl (cheap) → rerun the full consumer probe once per iteration round (it re-tests selection and flow, not just the fix). When done: stop the dev server background task, run the repo's tests/ci:check, and fold genuine provider quirks into code comments or tests so the next agent doesn't rediscover them.