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

Annotations already establish readOnlyHint=true and destructiveHint=false, so no safety warning is needed. The description goes beyond this by disclosing the exact return payload, the interpretation signal ('high consensus...safe for board communications'), the data source, and the per-call cost. This is rich, non-obvious behavioral context.

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 front-loaded with the most important use case, then proceeds through returns, an illustrative example, interpretation, source, and cost. Nothing is redundant, and every sentence earns its place without bloating the definition.

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?

With no output schema, the description correctly carries the return-value burden by listing consensus score, sentiment mix, key themes, and platform breakdown. It also adds source and price context. The main completeness gap is the unexplained optional `category` parameter, plus the consensus scale (0–1) is only inferred from the 0.78 example rather than stated.

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 0%, so the description must compensate. It does clarify what `topic` can be (vendor, category, trend, business topic) and gives a concrete example. However, the optional `category` parameter is never mentioned or explained, and no value format is given for either parameter, leaving a meaningful gap.

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 names a specific verb and resource: researching how AI systems characterize a topic across multiple platforms, returning a consensus score, sentiment mix, key themes, and platform breakdown. It clearly distinguishes itself from generic research or benchmark tools by focusing on cross-platform AI consensus, and the concrete healthcare example removes ambiguity.

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 opening 'Use when researching...' is an explicit condition for invoking the tool, and the scope is clear: vendors, categories, trends, or business topics across multiple platforms. However, it does not mention when not to use it or name a sibling alternative (e.g., get_platform_divergence or get_web_research_synthesis), so exclusions are left to inference.

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