get_llm_cost_by_model

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

No annotations are provided, so the description carries full burden. It mentions that the tool shows costs, tokens, and cheaper alternatives, which implies a read-only operation. However, it does not disclose authentication requirements, rate limits, data freshness, or any side effects (none expected). The description provides adequate but not comprehensive behavioral context.

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 well-structured with a purpose statement, a list of outputs, an arguments section, and example queries. It is front-loaded with the key purpose. While slightly verbose, the structure aids readability and every sentence adds value. A minor reduction in verbosity could improve conciseness.

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 read-only reporting tool with 2 simple parameters and no output schema, the description covers the key aspects: purpose, outputs, arguments with defaults, and example usage. It is mostly complete, though it could mention data freshness or how the cost-per-1M-tokens is calculated.

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 has 2 parameters with no descriptions, resulting in 0% schema description coverage. The description compensates by explicitly explaining each parameter: 'days: Lookback window in days (default 30).' and 'provider: Filter to a specific provider...'. This adds significant meaning beyond the raw schema.

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 that the tool breaks down AI/LLM costs by individual model with efficiency metrics. It lists specific outputs (cost per model, tokens, cost per 1M tokens, cheaper alternatives) and provides example queries that align with these capabilities. The name and description are consistent, and the tool is distinct from siblings like get_llm_costs or get_bedrock_costs.

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 examples of when to use the tool (e.g., 'Which model costs the most?', 'Show me OpenAI model cost breakdown'), but it does not explicitly state when to prefer this tool over alternatives like get_llm_costs or get_ai_spend_monitor. No exclusions or conditions are provided.

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