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get eval

get_eval
Read-onlyIdempotent

Fetch an eval run's live status, progress ratio, and full results when DONE—win rate with CI, W/T/L, latency, cost, and pass rates. Poll after creating a run.

Instructions

Fetch one eval run's status, progress and — once DONE — its full results (per-arm win rate with CI, W/T/L, latency, eval cost, savings, or corrected pass rates for criterion runs); poll this after creating a run. GET /v1/evals/{id} (API-key scope: read). Returns: The run object: {id, name, rubric, rubric_type, eval_kind, criterion_snapshot, baseline_model, candidate_models, arms:[{key, model, label, system, tools, n}], judge_model, sample_count, sample_filters:{model?, tag?, segment?, dataset_id?, trace_replay?, screening?}, status (PENDING|RUNNING|DONE|ERROR|CANCELLED), error (null, a failure reason, or "Cancelled by "), results (see list_evals for the comparison / criterion / screening shapes), assertions, created_at, progress_ratio (0..1; completed inference units over total — feed a progress bar)}. Notes: 404 {code:"not_found"} when the run is not in this key's workspace. results is null until DONE. A DONE screening's results.screening carries the similarity lens (match rate — never part of win/loss), quality-vs-cost placement per candidate and a recommendation (a "keep" is a first-class good outcome). Read-only, no spend.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesThe eval run id returned by POST /v1/evals.
Install Server

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description goes well beyond annotations by disclosing that results is null until DONE, that a 404 indicates the run is not in the key's workspace, how progress_ratio is computed, and the screening result semantics. No contradiction exists.

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 long, but the complexity of the return object justifies the length. It is front-loaded with the core purpose, followed by endpoint, response shape, and edge-case notes. Every sentence carries useful information, and there is no filler.

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?

There is no output schema, so the description carries the full burden of explaining the return value. It does this thoroughly: listing the run object's fields, status enum, error semantics, results behavior, and progress_ratio. For a complex polling tool, this is complete enough for an agent to call and interpret the response correctly.

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?

There is only one parameter, id, and the schema already fully documents it as 'The eval run id returned by POST /v1/evals.' Since schema description coverage is 100%, the baseline is 3. The description adds endpoint context and key-scope information, but not substantial new parameter meaning beyond what the schema already provides.

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 opens with a specific verb and resource: 'Fetch one eval run's status, progress and — once DONE — its full results.' This clearly identifies the tool as a single-run polling/retrieval operation, distinguishing it from siblings like list_evals and compare_evals without ambiguity.

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 explicitly tells the agent when to use this tool: 'poll this after creating a run.' It also clarifies that results are null until DONE, which guides polling behavior. It does not explicitly enumerate when not to use alternatives, but the usage context is clear enough.

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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