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

With no annotations, the description carries the full behavioral burden but says nothing about pricing source or freshness, behavior for unknown model names, or whether the comparison is computed on a single token workload. For a mutation-free but analysis-heavy tool, the agent learns only the bare operation.

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?

A single compact sentence with the operation front-loaded and zero filler. It is efficient, but the brevity comes at the cost of content rather than trimming redundancy.

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?

There is no output schema and no annotations, so the description should explain the return shape (e.g., per-model cost comparison) and any error behavior for invalid models. Instead it leaves an agent unable to anticipate the result structure.

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 all three parameters (model_names, input_tokens, output_tokens) are already documented in the schema. The description adds no extra meaning such as whether model names must match a known pricing catalog or whether token counts are per-model or shared.

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?

States a specific verb (compare) and resource (costs for multiple LLM models), and the plural 'multiple models' signals the batch nature of the operation. It does not, however, distinguish itself from close siblings like estimate_cost, predict_cost, or get_all_pricing, so an agent must infer the boundary.

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?

There is no guidance on when to use this instead of estimate_cost (single-model cost), predict_cost (forecasting), or get_all_pricing (raw price listing). The description provides no prerequisites, no exclusions, and no routing cues among a crowded set of cost/pricing siblings.

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