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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds useful behavioral context: it returns top brands, trend narrative, themed bullets, and names the data source (Stratalize AI citation composite). This goes beyond the schema and annotations, though it doesn't disclose limitations like data recency or sector coverage boundaries.

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 compact and front-loaded with the primary use cases, then details the return content, a concrete example, and the source. Every sentence adds value, and there is no redundant restatement of the tool name or schema. It is appropriately sized for the tool's simplicity.

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?

For a single-parameter, read-only tool with no output schema, the description covers the necessary context: when to use it, what it returns, a working example, and the data source. The low complexity means this is sufficient for an agent to select and invoke the tool correctly without additional detail.

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 provides only a generic 'sector' string with no description or enum, and schema description coverage is 0%. The description compensates partially by noting 'any equity sector' and giving the example 'Financials,' but it does not specify acceptable formats, capitalization, or possible values. The guidance is helpful but incomplete.

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 tool returns top brands by AI mention share, sector trend narrative, and themed bullets for any equity sector. It gives a concrete example (Financials) and names specific use cases, making the purpose specific and actionable. It does not explicitly contrast with sibling tools like get_category_ai_leaders, so it falls short of a 5.

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 explicitly states when to use the tool: 'Use when producing equity research, tracking brand share in AI sector coverage, or benchmarking a company AI visibility against sector peers.' This provides clear contexts, but it does not mention when not to use it or name alternative tools, so it lacks the full exclusion guidance of a 5.

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