Model drift status

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

Annotations cover the safety profile (readOnly, idempotent, non-destructive, openWorld), and the description goes further by disclosing what the payload actually contains: a per-model drift signature, a regime classification, per-benchmark-suite change-point detection, and axis-level attribution. With no output schema, this content disclosure is the main behavioral value-add; only return formatting/pagination is unmentioned.

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

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

Front-loaded with the purpose, then the returned fields, then the usage questions. The middle field list is dense and slightly enumerative — several items restate the same 'what changed' idea (change point, last significant change, axes moved) — but nothing is filler.

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

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

For a single-parameter read with no output schema, enumerating the response contents is exactly the missing piece and it is supplied. Nothing an agent needs in order to call it correctly is absent; only edge cases (unknown model, insufficient data) are unaddressed.

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

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

Schema coverage is 100% and the single 'model' parameter is fully documented in the schema, including its accepted forms (numeric id, name, display name) and a pointer to get_leaderboard. The description adds nothing parameter-level, so the baseline 3 applies.

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

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

The description names a specific verb+resource — measuring whether an LLM's measured performance has shifted — and enumerates the exact sub-signals returned (regime, score vs baseline, change-point detector, last significant change, moved axes). That level of detail lets an agent separate it from get_model and get_model_history without opening a schema.

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

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

It supplies concrete triggering questions ('has model X got worse?', 'is X being nerfed?'), which is strong positive guidance. It stops short of naming an alternative tool or stating when not to use it (e.g., versus get_model_history for raw history), so no exclusions are provided.

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