Get Model Pricing

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds valuable context beyond annotations: it specifies that it returns pricing details (input/output costs, context window, max output) and error handling (error message with suggestions if model not found), which are not captured in annotations.

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 and front-loaded with the core purpose, followed by clear sections for Args, Returns, and Examples. Each sentence adds value without redundancy, and the examples are concise and illustrative, making it easy to scan and understand.

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?

Given the tool's low complexity (1 parameter, no output schema), the description is mostly complete: it explains the purpose, parameter usage, return details, and error behavior. However, it lacks explicit guidance on when to use this tool versus siblings, which is a minor gap in contextual completeness for a server with multiple related tools.

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%, with the schema fully documenting the 'model' parameter. The description adds minimal semantics beyond the schema—it provides examples of model IDs (e.g., 'gpt-5', 'claude-sonnet-4.6') but no additional constraints or usage notes. This meets the baseline of 3 when schema coverage is high.

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 the specific action ('Get pricing details') and resource ('for a specific LLM model'), distinguishing it from siblings like 'list_models' (which lists models) or 'compare_models' (which compares multiple). The examples reinforce this specificity by showing concrete model lookups.

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 implies usage context through examples (e.g., looking up specific models like 'gpt-5'), but does not explicitly state when to use this tool versus alternatives like 'tokencost_list_models' (for browsing) or 'tokencost_compare_models' (for comparisons). It provides clear input-output behavior but lacks explicit guidance on tool selection.

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