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

With no annotations provided, the description carries the full burden. It lists what data is returned (verdict, snapshots, proof, URLs), implying a read-only lookup, but does not disclose error behavior, permissions, or any side effects. It gives a solid overview but stops short of fully transparent behavior.

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?

The description is a single, information-dense sentence that front-loads the purpose and lists the returned items. It is not overly verbose, though the run-on structure slightly hampers scannability. Every clause adds content.

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 simple one-parameter tool with no output schema, the description covers the returned data comprehensively. It lists all key output components, though it does not explain domain-specific terms like 'negative snapshots' or 'Bitcoin-anchored' – these may be understood from context or require additional tools. Overall adequate for the tool's simplicity.

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 description coverage is 100%, so baseline is 3. The description adds a concrete example ('Qwen/Qwen3-32B') and explains the format as 'Hugging Face id', reinforcing the schema's 'org/name' hint. This extra context nudges it above baseline.

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 clearly states the tool returns observed data about a single AI model, specifying the exact data items (verdict, snapshots, proof, etc.). It distinguishes itself from siblings like search_models (which searches) and get_silence_proof (which retrieves a specific proof) by focusing on a single model's full observed profile.

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?

The usage is implied: you have a model id and want its observed details. There is no explicit guidance on when to use this vs. alternatives, nor any when-not-to-use conditions. It could have mentioned that search_models is for finding models, but it doesn't. The context is clear enough to infer usage.

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