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Compare Perf To Baseline

compare-perf-to-baseline
Read-onlyIdempotent

Compare a run (runId) or filtered target (testchimpId plus optional dimensions) to its promoted baseline. envClass is required. Optional thresholds override max p95 regression percent and maximum fail-rate increase.

Input Schema

NameRequiredDescriptionDefault
botIdNoQA bots: your botId from your stored TestChimp binding.
runIdNo
datasetNo
llmModeNo
profileNo
envClassYes
environmentNo
testchimpIdNo
projectApiKeyNoQA bots: your own project API key from your stored TestChimp binding. Required on every call except get-bot-credentials, get-project-credentials and get-bot-compat. Never print it.
maxFailRateIncreaseNo
maxP95RegressionPercentNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / botId
      Added value: +{
      +  "description": "QA bots: your botId from your stored TestChimp binding.",
      +  "maxLength": 64,
      +  "minLength": 1,
      +  "type": "string"
      +}
    • addedInput schema / properties / projectApiKey
      Added value: +{
      +  "description": "QA bots: your own project API key from your stored TestChimp binding. Required on every call except get-bot-credentials, get-project-credentials and get-bot-compat. Never print it.",
      +  "maxLength": 256,
      +  "minLength": 8,
      +  "type": "string"
      +}
  2. First observed

TDQS

B3.4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavior beyond that: envClass is mandatory and the threshold parameters override the default p95 regression and fail-rate limits. It does not describe the comparison result or how the baseline is resolved.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three compact sentences, front-loaded with the core compare action followed by the required and optional constraints. No filler, though 'optional dimensions' is vague enough to be a wasted phrase.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 11 parameters at 18% coverage and no output schema, the definition should do more: it never explains what the comparison returns (pass/fail, regression details) or what the 'dimensions' actually are. It covers the essentials for invocation but leaves meaningful gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 18%, so the description must carry more weight. It clarifies runId, testchimpId, envClass, and the two threshold parameters, but leaves the 'optional dimensions' undefined and says nothing about dataset, llmMode, profile, or environment, leaving several params undocumented anywhere.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (compare) and resource (a run or filtered target against its promoted baseline), and distinguishes the two targeting modes (runId vs testchimpId plus dimensions). It does not explicitly differentiate itself from siblings like get-perf-run, list-perf-baselines, or promote-perf-baseline, so it falls short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use it by naming the two input modes and the required envClass, but offers no explicit when-not conditions and never points to an alternative tool (e.g., promote-perf-baseline to set a baseline, list-perf-baselines to inspect one). Usage is inferable but not guided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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