model_drift

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

No annotations provided, so description carries full burden. It discloses that the data is from a 'public, frozen eval suite' and that metrics include accuracy, latency, answer length, reliability, refusal rate. It also mentions weekly runs and drift tracking. While rate limits or auth are not mentioned, the read-only nature is clear from 'look up'. A minor gap is absence of explicit read-only declaration, but context sufficient.

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?

Two well-structured sentences. First sentence states the action and key metrics; second sentence provides use case and optional parameter usage. No fluff.

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?

Given no output schema, the description enumerates the returned metrics (accuracy, latency, answer length, reliability, refusal rate) and mentions the comparison to previous weekly run. For a simple lookup tool with one optional parameter, this is complete enough for an agent to understand input and output.

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?

Only one parameter 'model' with schema description. The description adds concrete examples ('gpt-5', 'claude-opus', 'gemini') and the behavior when omitted ('list every tracked model'). Schema coverage is 100%, yet description still adds value beyond the schema.

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 uses a specific verb phrase 'Look up how a live LLM is currently scoring on a public, frozen eval suite' and clearly identifies the resource (model performance metrics). It distinguishes from sibling tools like calc or search by focusing on drift detection.

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?

Explicitly states when to use: 'Use it to check whether a model you're about to rely on has quietly changed.' Provides an alternative usage: 'Omit model to list every tracked model.' No ambiguity.

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