Skip to main content
Glama

evalsnap

You re-ran your agent and the answers look slightly different — but did anything actually get worse? evalsnap takes two snapshots of case outputs, a baseline run and a current run, matches them by case_id, and tells you exactly what moved:

  • Matched, added, removed — cases present on both sides, cases that only exist in the current run, and cases that disappeared from the baseline, each list sorted by id

  • Per-case results — for every matched case: whether the output is unchanged or changed under exact string equality, the score delta and the duration delta (each null when either side did not record that measurement), and a regression flag that is set only when the score went down

  • Regressions and improvements — the sorted list of regressed case ids and the count of cases whose score rose

  • Average scores — each side averaged over the matched cases that carry a score on both sides, so the comparison covers the same set on both sides; both averages are null when no matched case qualifies

  • Verdict — fail when at least one regression exists, otherwise pass; added and removed cases are reported but never fail the verdict on their own

Deltas and averages are rounded to four decimal places. The computation is pure and local: no network, no configuration, nothing read beyond the snapshots you pass in. Invalid input — an empty snapshot, a case without an id or output, a duplicate id inside one side, a non-numeric score, a negative duration — is rejected with an error naming the offending path and side, and no partial diff is produced.

CLI

npx @reneturcios/evalsnap diff --input args.json
npx @reneturcios/evalsnap --version

args.json contains the full arguments object:

{
  "baseline": [
    { "case_id": "greeting", "output": "hello", "score": 0.9, "duration_ms": 120 },
    { "case_id": "summary", "output": "short text", "score": 0.7 }
  ],
  "current": [
    { "case_id": "greeting", "output": "hello", "score": 0.95, "duration_ms": 110 },
    { "case_id": "summary", "output": "short text", "score": 0.6 }
  ]
}

score and duration_ms are optional per entry. The diff is printed as JSON.

Exit codes: 0 on success, 1 on a runtime error (including an unreadable or invalid input file, or invalid input), 2 on a usage error.

Related MCP server: ghidriff-mcp

MCP server

npx -y @reneturcios/evalsnap mcp

MCP client config:

{
  "mcpServers": {
    "evalsnap": {
      "command": "npx",
      "args": ["-y", "@reneturcios/evalsnap", "mcp"]
    }
  }
}

Tools

  • diff_snapshots — Diff two eval snapshots matched by case_id.

    • baseline (array, required, at least one) — entries as {case_id, output, score?, duration_ms?} objects

    • current (array, required, at least one) — same entry shape

    Returns {total_baseline, total_current, matched, unchanged, changed, added, removed, case_results, regressions, improved_count, baseline_avg_score, current_avg_score, verdict}.

Docker

docker run -i ghcr.io/cloudcorprecords/evalsnap
docker run --rm -v "$PWD:/w" ghcr.io/cloudcorprecords/evalsnap diff --input /w/args.json

Development

npm install
npm run build   # tsc -> dist/
npm test        # tsx --test test/*.test.ts (pure local, no network)
npm run dev     # run the CLI from source

License

MIT — Copyright (c) 2026 Rene Turcios

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Related MCP Connectors

    Related MCP Servers

    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables you to audit your AI agent skills by running each one against an agent that cannot see it, diffing the resulting artifacts, and grading whether each skill genuinely improves, changes nothing, or worsens the output.
      6 npm
      MIT
    • A
      license
      A
      quality
      C
      maintenance
      Enables AI agents to perform Ghidra binary diffing, returning structured match statistics, function changes, and code diffs as JSON.
      13
      2
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables reverse-engineering agents to export binaries to BinExport format and compare two builds, returning matched, changed, and build-unique functions with addresses, names, similarity, and confidence scores. Diff results are written as JSON files with overall similarity and confidence summaries.
      1
      MIT