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* fix: address missing/incorrect steps in readme for initial deploy (and applied them) * fix: unblock workflows for initial deploy
Add the mcp-evals package: loads eval cases from a Google Sheet or CSV, runs the in-process mcp-agent against each query, and scores tool use, dataset surfacing, and answer quality (Mistral judge). Includes the CLI, example cases, tests, a manual workflow_dispatch CI workflow, and docs.
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Splits the evals harness out of the
evals-frameworkbranch into its own PR.What
Adds the
mcp-evalspackage — an evaluation harness that runs the in-processmcp-agentagainst eval cases and scores it:mcp-evals runCLI, example cases, and tests..github/workflows/evals.yml: manualworkflow_dispatchonly — evals are slow, nondeterministic, and billed, so deliberately not in PR/push CI. It serves the chosen toolset locally and uploads result CSVs as an artifact.Notes
pyproject.tomlgains themcp-evalsworkspace member;uv.lockregenerated..~lock.gold.csv#).cds-eqc-tooling).