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

With no annotations, the description carries the burden of explaining behavior. It discloses what the tool computes, that it ranks all 18 models, that it reports cheapest and most expensive options and cost spread, and that it filters based on whether models can fit the prompt. It could mention data-source assumptions or defaults, but the core behavior is well covered.

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?

Two sentences deliver the core purpose, the workload formula, and the reported outputs with no filler. The ranking behavior is front-loaded and the context-window check is a valuable addition that earns its 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?

The tool has no annotations and no output schema, so the description must cover enough for correct invocation. It explains inputs, ranking logic, and key outputs. Minor gaps remain around data-source recency, currency, and how optional parameters behave when omitted, but the essential context is present.

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 the baseline is 3. The description adds meaning beyond the schema by explicitly defining the cost formula: inputTokensPerCall, outputTokensPerCall, and callsPerMonth combine to determine monthly cost, and minContextWindow relates to whether a prompt fits. This adds useful semantics beyond basic field descriptions.

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 action and resource: 'Rank 18 LLM models by the real monthly cost of a given workload,' using the formula input/output tokens per call × calls per month. It clearly distinguishes this from sibling tools like compare_ai_coding_tool_costs and compare_automation_platform_costs by targeting LLM model pricing.

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 clear context for when to use the tool: when comparing LLM models on cost for a specific workload and context-window fit. It doesn't explicitly name alternatives or exclusions, but the phrase 'LLM models' plus the detailed workload formula makes the intended use obvious relative to the financial and infrastructure siblings.

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