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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds valuable context by noting 'Prices as of 2025 — verify,' which signals potential inaccuracy. It also promises an 'input vs output cost split,' giving a hint about the output. However, it does not disclose details such as whether prices are hardcoded, whether the tool makes external calls, or the exact output format.

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 concise at two sentences. The first sentence begins with a repetition of the title ('LLM API Cost Calculator (GPT-4o, Claude, Gemini)') which is slightly redundant, but it quickly moves to the core functionality. The second sentence and final caveat are efficient and informative, earning their place.

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 four numeric parameters and an enum, the description covers the purpose and key inputs. It mentions the input/output cost split, which partially describes the output. However, it does not state the exact return format or any assumptions (e.g., per-token pricing, no caching). Given that no output schema exists, the description could be more specific about what the user sees.

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 partially does by mentioning 'from tokens per request and volume,' which maps to inputTokensPerReq, outputTokensPerReq, and requestsPerMonth. It also names the model families (GPT-4o, Claude, Gemini) that align with the enum. However, it does not define each parameter precisely or explain constraints, so the compensation is incomplete.

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 'Estimate monthly API costs for GPT-4o, Claude, and Gemini from tokens per request and volume,' which specifies the verb (estimate), resource (API costs), and scope (specific models). It also mentions 'See input vs output cost split,' further clarifying the tool's function. This distinguishes it from sibling tools like token_counter_calculator or fine_tuning_cost_calculator.

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 provides clear context for when to use the tool: when estimating monthly API costs from token counts and request volume. It does not explicitly mention alternatives or exclusions, but the context is unambiguous. The 'Prices as of 2025 — verify' note adds a temporal constraint that informs usage.

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