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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: it explains the scoring scale (0.91 = near-universal consensus, below 0.50 = high divergence), the source (Stratalize multi-platform citation composite), and the pricing ($0.02 per call). This goes beyond what annotations provide.

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 core purpose. The examples and pricing are useful additions, though the pricing detail could arguably be moved to a separate field. Overall, every sentence contributes to understanding the tool's function.

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 key aspects: what it does, what the output means, how to interpret scores, and the data source. It doesn't describe the exact output structure, but the absence of an output schema makes that less critical given the clear explanation of the scoring scale.

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. The description explains that brand_name is the vendor or topic to analyze, and the examples (Salesforce, niche vendors) clarify what values are appropriate. It doesn't specify format requirements, but the single parameter's meaning is well-illustrated.

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's purpose: identifying gaps between AI platform recommendations and actual market position for a vendor or topic. It uses specific verbs ('identifying gaps', 'Returns platform agreement score') and distinguishes itself from siblings like get_ai_consensus_on_topic and get_market_intelligence_brief by focusing on cross-platform agreement/disagreement.

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 provides concrete usage context with examples (Salesforce vs niche vendors) and explains what the output means (high agreement vs divergence signals content gap opportunity). It doesn't explicitly name alternative tools to use instead, but the examples and focus on platform consensus make the use case clear.

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