Compare TokenLab Models

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

Annotations already indicate the tool is read-only, idempotent, and non-destructive. The description adds no further behavioral context (e.g., output format, rate limits, or open-world implications). With high annotation coverage, the description's lack of extra detail is acceptable, earning a baseline 3.

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, efficient sentence with no wasted words. It directly conveys the tool's purpose without extraneous information.

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 the tool's simplicity, two parameters, and comprehensive annotations, the description covers the essential functionality of comparing model details and pricing. It does not describe the return structure, but the absence of an output schema makes this less critical. Slightly incomplete due to the missing parameter explanation, but overall adequate.

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 0%, so the description must compensate. It clarifies that 'models' is a list of model IDs and that the tool compares details and pricing. However, it fails to explain 'include_raw', leaving one of two parameters undocumented. This partial coverage justifies a score of 2.

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 'Compare' and identifies the resource 'public TokenLab model details and pricing' for 'several model IDs'. This clearly distinguishes the tool from siblings such as 'get_model' (single) and 'list_models' (all).

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 description implies usage for comparing multiple models but does not explicitly state when to use it versus alternatives or provide when-not-to-use guidance. A minimal viable score is appropriate as the context is implied but not articulated.

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