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run_check

Compare test results against a golden baseline to detect regressions. Returns diff summaries and flags score drops as blocking failures, with warnings for behavioral shifts.

Instructions

Check for regressions against the golden baseline. Returns a diff summary for each test: PASSED, OUTPUT_CHANGED, TOOLS_CHANGED, or REGRESSION. REGRESSION means the score dropped significantly — treat this as a blocking failure. TOOLS_CHANGED / OUTPUT_CHANGED are warnings: the agent's behavior shifted but may be intentional. Also returns observability signals: behavioral anomalies (tool loops, stalls), trust scores (benchmark gaming detection), and coherence issues (multi-turn context loss). Use this after any code change (prompt, model, tools) to confirm nothing broke. If you see a regression, show the diff to the user and offer to fix it before moving on. Use heal=true to auto-retry flaky failures and distinguish non-determinism from real drift. IMPORTANT: Automatically detect test_path by looking for a 'tests/evalview/' directory in the current project. If it exists, pass it as test_path. If the project has a custom test location, use that instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
testNoCheck only this specific test by name (optional, checks all by default)
test_pathNoPath to the test directory. Auto-detect: use 'tests/evalview/' if it exists, otherwise 'tests'.
healNoAuto-retry flaky failures, propose candidate variants, distinguish non-determinism from real regressions. Default: false.
strictNoFail on any change (REGRESSION, TOOLS_CHANGED, OUTPUT_CHANGED). Default: false.
ai_root_causeNoUse AI to explain low-confidence regressions with root-cause analysis. Default: false.
statisticalNoRun each test N times for variance analysis (e.g. 5). Omit for single run.
auto_variantNoAuto-discover distinct execution paths as golden variants. Requires statistical. Default: false.
budgetNoMaximum total budget in dollars (e.g. 0.50). Remaining tests skipped when limit hit.
dry_runNoPreview test plan and estimate cost without executing. Default: false.
tagNoCheck only tests tagged with these behaviors (OR match). E.g. ['tool_use', 'retrieval'].
fail_onNoComma-separated statuses to fail on (default: REGRESSION). E.g. 'REGRESSION,TOOLS_CHANGED'.
timeoutNoTimeout per test in seconds (default: 120).
reportNoGenerate HTML report at this path (auto-opens in browser).
judgeNoJudge model for scoring (e.g. 'gpt-5', 'sonnet').
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses return statuses, observability signals, auto-retry with heal, and auto-detect logic. Could mention auth or rate limits but not critical.

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

Conciseness3/5

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

Description is lengthy (several sentences) and repeats auto-detect logic. Front-loaded with main purpose but could be more concise. Every sentence adds value but some redundancy.

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

Completeness5/5

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

Covers return values (diff summary, observability signals), how to interpret results (blocking vs warnings), usage triggers, and all 14 parameters. No output schema but description compensates effectively.

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

Parameters4/5

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

Schema coverage is 100% (baseline 3). Description adds value beyond schema: explains auto-detect for test_path, heal's purpose, fail_on defaults, and budget behavior. Provides usage context like 'auto-discover distinct execution paths as golden variants'.

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

Purpose5/5

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

The description clearly states the tool checks for regressions against a golden baseline and returns specific diff statuses. It uses specific verbs ('Check for regressions') and distinguishes from sibling tools like compare_agents or create_test.

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

Usage Guidelines5/5

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

Explicitly says 'Use this after any code change' and provides when to use heal, how to auto-detect test_path, and what to do if regression found ('show the diff to the user and offer to fix'). No alternatives mentioned but context is clear.

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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