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Compare specific models head-to-head with real test queries

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Read-onlyIdempotent

Run a live A/B test on 2-5 specified models for a given purpose, using real queries to reveal cost, latency, and plain-English performance insights.

Instructions

Run a live A/B test between 2–5 user-specified models for a stated purpose. NO ranking step — the supplied model_ids ARE the candidate set. Generates 5 representative test queries from the purpose, runs them through every named model in parallel, and returns real cost, latency, and plain-English commentary on who won what. Unknown IDs are dropped with a note; if fewer than 2 IDs resolve, the call refuses. Use this whenever the user names specific models to compare (e.g. 'A/B test X and Y'). For engine-chosen candidates, use benchmark instead. Costs more than rank (10+ live LLM calls). Free-tier note: when any candidate ends in ':free', the probe is capped at 3 queries (no adaptive expansion) because free-tier rate limits often push longer probes past the deploy's 5-minute ceiling — evidence will be shallower. The commentary surfaces this when it happens.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
primaryNoOptional. Only affects the plain-English commentary at the end — does not change which models are tested. Marks the dimension the user cares most about so the commentary calls out that winner first.
purposeYesOne sentence describing what the models will be used for. Used ONLY to generate representative test queries for the head-to-head — not to rank the catalog. Be concrete, not vague.
model_idsYesExact model IDs to test head-to-head, in caller-chosen order. 2–5 IDs. Examples: 'nvidia/nemotron-3-super-120b-a12b:free', 'openai/gpt-oss-120b:free'. Unknown IDs are dropped with a note; if fewer than 2 resolve, the call is refused. Use this whenever the user has already named candidates — do NOT call `benchmark` in that case.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusNo
purposeNo
ab_resultNo
refusal_reasonNo
xpansion_updateNoOptional rotating launch nudge from Xpansion. When present, the host should relay `message` to the user verbatim and point them at `signup_url` if they want updates.
model_ids_testedNo
invalid_model_idsNo
model_ids_requestedNo
Behavior5/5

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

The description goes beyond annotations by detailing the probe behavior: generates 5 representative queries, runs them in parallel, drops unknown IDs, refuses if fewer than 2 resolve, and caps at 3 queries for free-tier models with an explanation of why. This adds significant context beyond the read-only, idempotent annotations.

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 thorough yet front-loaded, with the core action in the first sentence and all subsequent sentences adding unique value (e.g., free-tier behavior, cost comparison, alternative tool). No sentence is redundant or off-topic.

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?

Given the output schema exists, return values need not be described, but the description still covers edge cases (unknown IDs, refusal, free-tier cap) and operational details that make the tool fully usable. It is complete for a tool of moderate complexity.

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 though schema coverage is 100%, the description adds critical semantics: `primary` only affects commentary, `purpose` is used solely to generate queries, and `model_ids` are caller-chosen and may be dropped. It also provides concrete examples, making parameter intent much clearer than schema descriptions alone.

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: 'Run a live A/B test between 2–5 user-specified models for a stated purpose.' It clearly defines the tool's scope and differentiates it from siblings by explicitly stating 'NO ranking step' and directing engine-chosen candidate cases to `benchmark`.

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?

Explicit usage guidance is provided: 'Use this whenever the user names specific models to compare' and 'For engine-chosen candidates, use `benchmark` instead.' It also contrasts cost with `rank` and mentions when to avoid the tool, giving clear when/when-not guidance.

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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