analyze_brand

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

No annotations are provided, so the description bears full responsibility. It discloses that results are saved to a dashboard (side effect) and outlines return data types. However, it does not mention auth requirements, rate limits, or failure conditions. For a tool with no annotations, this is adequate but not comprehensive.

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 concise sentences with no filler. The first sentence immediately states the action and scope (analyze brand across AI models) and the second lists outputs and side effect. Every sentence earns its place.

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 no output schema, the description lists return components (overall score, per-model scores, etc.) but does not explain the score range or calculation methodology. For a complex analysis tool, more detail on output semantics would improve completeness. The side effect (dashboard save) is noted.

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 already provides good descriptions for all three parameters (100% coverage). The description adds value by explaining how competitors are used ('competitor comparison') and that market defaults to 'global'. It reinforces the purpose of each parameter without being redundant.

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 it analyzes brand visibility across multiple specific AI models (Claude, GPT-4o, etc.) and returns structured outputs like an overall score, per-model scores, sentiment, competitor comparison, and recommendations. It also mentions a side effect (saving to dashboard). This differentiates it from the sibling tool get_brand_history, which is likely historical in nature.

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 does not explicitly state when to use this tool versus the sibling get_brand_history. It implies use for current visibility analysis across models, but lacks exclusions or alternative scenarios. The sibling tool name suggests historical data, but no direct guidance is provided.

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