acedatacloud_get_model

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

With no annotations provided, the description carries full burden. It discloses the auth requirement (no token) and matching behavior (case-insensitive substring), but lacks details on error handling, rate limits, or what happens if model is not found.

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 with no wasted words. First sentence covers action and return, second sentence covers auth. Highly efficient.

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 has one parameter and an output schema (implied), the description is largely complete. It explains the lookup method, return content, and auth. Missing potential edge cases like 'not found' behavior, but overall adequate.

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 schema already provides a description for the 'model' parameter. The tool description adds value by explaining the case-insensitive substring matching behavior, which goes beyond the schema's 'id or name' description.

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 verb 'look up' and resource 'models', specifies case-insensitive substring matching by id/name, and mentions the return of credit pricing and capabilities. This distinguishes it from sibling tools like acedatacloud_list_models.

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 provides clear context for when to use (look up specific models) and explicitly states no token is required. However, it does not explicitly mention when not to use or suggest alternatives like list_models for browsing.

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