token_cost

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

With no annotations provided, the description carries the full behavioral disclosure burden, and it does well. It explains that costs are computed with input/output priced separately and a prompt-cache discount, that defaults mirror an interactive calculator, and that the response includes 'result, formula, interpretation and a ready-to-quote cite_as sentence.' This gives an agent a solid model of what will happen and what will be returned. It does not mention rate limits or edge cases, but for a stateless calculator those are less critical.

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 front-loaded with a purpose question, followed by pricing details, output format, parameter guidance, and response contents in just a few sentences. The URL and cite_as mention are useful and do not feel extraneous. Every sentence earns its place, and the structure moves from high-level purpose to operational details.

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 calculator with three optional parameters and no output schema, this description is complete. It covers what the tool computes, the pricing logic, default parameter behavior, the ranked output, and the response structure. An agent has enough information to call it correctly and to interpret the result without additional context.

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?

The input schema already documents all three parameters with units and default values, and schema description coverage is 100%, so the baseline is 3. The description adds the useful fact that all parameters are optional and that defaults mirror the interactive calculator, but it does not substantially deepen meaning beyond the schema. The prompt-cache discount behavior mentioned in the description does add context for the 'cache' parameter, but not enough to push the score higher.

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 opens with a specific question: 'What does a monthly token volume cost across GPT, Claude, Gemini and DeepSeek?' and states concrete behaviors: separate input/output pricing, prompt-cache discount, and a ranked bill per model. This clearly distinguishes the tool from siblings like token_burn, which appears related but is not described. The verb 'returns' plus the resource 'ranked bill per model' makes the tool's function unambiguous.

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 gives a clear use case: computing monthly token costs across major model families, with input/output and cache considerations. It also notes that 'all parameters optional — defaults mirror the interactive calculator,' which tells an agent when no arguments need to be supplied. It does not explicitly name sibling alternatives or state when not to use them, but the scenario is clear enough for correct selection.

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