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get eval gate

get_eval_gate
Read-onlyIdempotent

Convert an eval run into a CI gate: returns 200 when all quality thresholds pass, 412 otherwise, to block unsafe model or prompt changes.

Instructions

Turn a finished eval run into a CI deploy decision with one call — 200 when every requested threshold passes, 412 otherwise — so a pipeline can curl -f it and block a bad model/prompt change. GET /v1/evals/{id}/gate (API-key scope: read). Returns: {pass: boolean, status: run status, checks:[{check: "win_rate"|"pass_rate"|"assertion_pass_rate"|"noninferiority", model, required, actual (CI lower bound or exact rate, null when unavailable), pass, note?}], reason? (set when the gate could not evaluate: run not DONE, run ERROR, or no thresholds given)}. HTTP 200 only when pass is true; 412 whenever anything failed. Notes: 412 (not 4xx-error shape — the verdict body itself) when: the run is not DONE ("Run not complete yet — poll until status is DONE." — fail closed), the run is ERROR, no threshold param was passed, or any check fails. Query values must parse as numbers in 0..1; anything else is treated as absent. 404 {error:{…}} when the run is not in this workspace. Keys in this response are NOT re-cased (they are already snake/single-word). Read-only, no spend. Pair it with a read-only scoped key for CI.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesThe eval run id.
modelNoRestrict the checks to one arm/model key (candidate key as listed in candidate_models).
min_win_rateNo0..1. Comparison runs: every candidate arm's win-rate 95% CI LOWER bound must be ≥ this (never the point estimate). Each check names its basis: "corrected" when the run carries ≥30 human pair labels and a usable calibration (the number to gate on — the PRINTED win rate is compressed toward 50% by judge error), else "printed" with a note saying how to attach labels (label_eval_pair). When the corrected point clears the bar but the calibration floor cannot, the note prices the missing labels instead of asking for more samples. Fails closed when the judge returned no verdict on too many pairs (unreportable).
min_pass_rateNo0..1. Criterion runs: every model's calibration-corrected pass-rate CI lower bound must be ≥ this; falls back to the observed CI when the judge is unvalidated (the check's note says so).
win_rate_tiesNoTie lever for the win-rate check. "half" (default): tie = half a win, parity 50%, comparable to the printed rate. "decided": ties dropped on both sides — wins/(wins+losses), the share among pairs someone decided; compresses less and needs fewer labels, answers a narrower question. Anything else is a 400.
noninferiority_marginNo0..1. THE CERTIFIED SWITCH TEST: on a criterion run whose baseline is "__stored__" (the incumbent's logged answers) scored by a calibrated judge, each candidate's pass-rate CI floor must reach the incumbent's rate minus this margin (0.05 = provably within 5 points at worst). Requires the stored-baseline arm AND a calibrated judge (corrected rates) — no observed-rate fallback; fails otherwise with an explanatory note.
min_assertion_pass_rateNo0..1. Every model's exact all-assertions pass rate must be ≥ this (deterministic count). Fails if the run has no assertions configured.
Install Server

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the readOnly/idempotent/non-destructive annotations, the description discloses critical behaviors: 412 is not an error shape but the verdict body, fail-closed on non-DONE runs, 404 semantics, invalid query values treated as absent, key casing, and 'Read-only, no spend.' This adds substantial context the annotations alone do not provide.

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

Conciseness5/5

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

The description is dense but every sentence carries useful operational information: verdict semantics, response shape, 412 cases, parsing rules, and authentication. It is front-loaded with the core one-call CI decision concept, and the details are organized logically from response shape to edge cases.

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?

With no output schema, the description fully compensates by specifying the return object shape, check types, the conditions for 200/412/404, the meaning of 'reason', and polling guidance. For a tool with 7 parameters and complex threshold semantics, nothing essential for an agent to invoke it correctly appears to be missing.

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

Parameters5/5

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

Even with 100% schema coverage, the description adds deep semantics beyond the schema: thresholds are compared against CI lower bounds, not point estimates; it explains corrected vs. printed rates, fail-closed behavior, calibration prerequisites, and the noninferiority switch test. This is a strong value-add over the raw parameter descriptions.

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 states a specific verb+resource+outcome: 'Turn a finished eval run into a CI deploy decision with one call' and gives the exact endpoint GET /v1/evals/{id}/gate. It clearly distinguishes the tool's role as a deploy gate from the broader eval-related sibling tools by emphasizing the 200/412 binary verdict for pipeline blocking.

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

Usage Guidelines4/5

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

The description clearly establishes the CI context: 'so a pipeline can `curl -f` it and block a bad model/prompt change' and advises pairing with a read-only scoped key. It doesn't explicitly name alternatives or specify when not to use this tool, but the intended workflow and fail-closed polling behavior are clear enough for an agent to select it appropriately.

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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