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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description does not need to restate safety. It adds valuable behavioral context: it reveals the output composition (top brands, trend narrative, themed bullets), the data source (Stratalize AI citation composite), and the cost ($0.10 USDC per call). This goes beyond annotations and helps the agent anticipate side effects (cost) and trust the data origin.

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 moderately long but every sentence earns its place: use cases, output composition, a concrete example, source attribution, and pricing. It is front-loaded with the primary use cases, and the example adds clarity without redundancy. It could be slightly more concise, but the structure is logical and information-dense, making it effective for an agent.

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 tool with a single parameter and no output schema, the description is highly complete. It specifies the input (sector), the output (brands by share, trend narrative, themed bullets), a realistic example, the data source, and the cost. An agent has all necessary information to decide when to call it and what to expect. There is no missing critical detail that would impair correct invocation.

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?

With schema description coverage at 0%, the description must clarify the parameter. It explains that 'sector' refers to 'any equity sector' and provides a concrete example ('Financials sector') with associated data. While it does not enumerate all possible sector values or specify format, the example and phrase 'any equity sector' give sufficient guidance for a single string parameter. This adequately compensates for the missing schema 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 states a clear, specific purpose: 'Returns top brands by AI mention share, sector trend narrative, and themed bullets for any equity sector.' It also provides a concrete example (Financials sector with specific brands and percentages) that illustrates the output. The verb 'returns' and resource 'AI mention share' make the tool's function unambiguous, and the example distinguishes it from generic benchmark tools.

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 lists 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.' It gives clear context but does not mention when not to use it or alternative tools. Given the large sibling list, this is a minor gap, but the use cases are specific enough to guide an agent.

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