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

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds meaningful behavioral context: how it probes each entity with ai_visibility_check, ranks results, and returns a ranked list with score, confidence, signal density. This supplements the annotations without contradiction.

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?

Three sentences with high information density: first sentence states core purpose, second explains the process, third provides a concrete use case and output details. No redundant or vague phrasing.

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 partially describes the output format (ranked list with score, confidence, signal density per entity). It covers the input parameters well and gives enough context for an agent to decide to use this tool. A full output schema would make it 5.

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 100%, so the baseline is 3. The description adds significant value: it explains the ordering of entities ('First entry treated as the 'subject''), clarifies model options (workers-ai default, anthropic requires API key), and describes the context parameter purpose. This goes well beyond the schema.

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 that the tool compares AI visibility across entities by probing with ai_visibility_check, ranks by score, and surfaces most/least recognized. It includes a concrete use case ('does Claude know about us as well as our competitors?') and distinguishes itself from sibling tools like ai_visibility_check (single probe) and compare_entities (generic comparison).

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 says it is 'useful for competitive AI-marketing audits,' providing clear usage context. It implies when to use it (comparing multiple entities) and hints at alternatives by mentioning ai_visibility_check as a building block, but lacks explicit when-not-to-use guidance.

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