get_content_gaps

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

Annotations already declare readOnlyHint=true, so the agent knows this is a safe read operation. The description adds context about what the tool produces (prioritized topics and formats, gap analysis vs competitors winning same prompts) but doesn't disclose things like whether recommendations require a tracked/refreshed brand or how current the data is. With readOnly annotation covering the safety profile, a 3 is appropriate.

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 fairly compact at three sentences and front-loads the core purpose. The example user phrases add practical value but could be tightened; still, it's efficient with no wasted sentences.

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?

The tool has a full input schema (100% coverage), an output schema, and clear readOnly annotations. The description explains what the output represents (prioritized topics/content formats to close gaps) and when to use it, which is sufficient given the rich structured data already present. It doesn't need to explain return values since an output schema exists.

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 coverage is 100%, so both parameters (brand_id, max_recommendations) are fully documented in the input schema. The description doesn't add parameter-specific details beyond what the schema provides, so baseline 3 is appropriate.

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 content recommendations based on AI visibility gaps, specifically prioritized topics and formats to close gaps versus competitors. It uses a specific verb (get) with a clear resource (content gaps) and distinguishes itself well from siblings like check_visibility or generate_prompts.

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 explicit example queries that should trigger this tool ('what should I write to improve AI visibility?', 'what content gaps do I have?', 'how do I get cited more by AI?'). While it doesn't name alternative tools to use instead, the clear trigger phrases effectively guide appropriate usage.

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