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Route the eval dataset through OpenRouter (same google/gemini-2.5-flash model) instead of the Google provider, add EVAL_MODEL to swap the model, and declare ai and @openrouter/ai-sdk-provider as dev dependencies. Add a weekly and on-demand MCP evals workflow modeled on Azure smoke. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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Summary
The eval dataset now reaches the model through OpenRouter instead of the Google provider. The model is still
google/gemini-2.5-flash, so pass rates stay comparable, andEVAL_MODELswaps it for any OpenRouter model ID.The workflow is modeled on
azure-smoke.yml. It adds what the cases need on the host:Evidence
aiand@ai-sdk/google, but neither was inpackage.json, so they only ran after a manual install. No workflow ran them.After:
yarninstalls the provider, and the tester resolves it:OPENROUTER_API_KEYsecret. Once this PR is merged, the workflow can be started by hand.Merge Danger
Door: two-way
Only test code, test data, dev dependencies and a new workflow change. Reverting the PR restores the Gemini setup.
Blast Radius: CI-only
OPENROUTER_API_KEYrepository secret. Without it the job fails immediately withMissing required environment variable.🤖 Generated with Claude Code