Analyze

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description says nothing about required permissions, rate limits, output format, model selection implications, or side effects. 'Analyze content using AI models' is a tautology that provides no behavioral context whatsoever.

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 a single seven-word sentence – extremely concise. However, its brevity comes at the cost of under-specification rather than efficient information delivery. It is front-loaded but contains no actionable detail.

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 4 parameters (3 required), no annotations, and an output schema. The description does not compensate for the missing annotations by explaining the AI model interaction, expected latency, or result format. Even with an output schema covering return values, the description is too thin for a multi-purpose AI tool with a generic name.

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 all four parameters are documented in the schema itself – including the analysis_type enum-like list and the optional provider field. The description adds no parameter meaning beyond the schema. Per rubric, baseline 4 applies for zero params, but here we have 4 params fully covered; a 3 reflects that the description contributes nothing beyond what the schema already provides.

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 'Analyze content using AI models' restates the tool name and title with a vague verb. It does specify the resource ('content') and mechanism ('AI models'), but 'analyze' is an umbrella term that doesn't distinguish this from siblings like 'chat' or 'generate'. An agent cannot confidently choose this over 'compare' or 'generate' based on the description alone.

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?

No guidance is given on when to use this tool versus alternatives. Siblings include 'compare', 'chat', 'generate', and 'list_models' – all potentially overlapping AI operations. The description never mentions these alternatives or the conditions under which 'analyze' is the correct choice.

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