get_model_history

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

No annotations are provided, so the description carries the full burden. It discloses the return type (historical price series) and a plan-dependent window for days, which is useful. However, it does not mention rate limits, data update frequency, granularity, or error behavior. For a read-only tool this is adequate but not rich.

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 focused and front-loaded with the main purpose, followed by differentiation context and an Args/Returns structure. It includes a useful example and parameter hints without excessive verbosity. The only slight redundancy is the differentiation paragraph, but it adds value.

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 is simple (2 parameters) and has an output schema, so return value details are not required. The description covers purpose, usage context, parameter constraints, and the return type, plus a plan-dependent caveat. It is complete enough for correct invocation, though it could mention data granularity or limitations.

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 no descriptions (0% coverage), but the description's Args section adds meaning: model_id with canonical example 'openai/gpt-5.6' and days with range (1-365, default 30) and plan-dependency. This compensates well for the schema's lack of detail.

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?

Clearly states the verb and resource: 'Get HISTORICAL price data / trends for one model.' It explicitly scopes to a single model and historical data, which distinguishes it from siblings like get_model (current price) and compare_providers. The differentiator against aggregators like OpenRouter is also highlighted.

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 explains when to use by contrasting with aggregators: 'aggregators like OpenRouter expose only current price; this returns the price over time, enabling trend analysis.' This implies use case (historical trends) but does not explicitly name sibling alternatives or exclusions, so it is clear but not exhaustive.

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