list_content_gapsList AI-generated content suggestions (FAQs, differentiators, use cases, about copy) the signed-in user can publish to close visibility gaps found in their latest scan.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, and the description adds useful context about the output being AI-generated, publishable suggestions for the signed-in user. This goes beyond the annotations without contradicting them.
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 a single, front-loaded sentence that starts with the action and efficiently packs in the resource type, scope, and source. No unnecessary words or repetition.
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 parameterless read-only tool with annotations and no output schema, the description covers the essentials: what is returned (a list of content suggestion types), for whom (signed-in user), and from where (latest scan). It is complete for an agent to invoke correctly.
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 zero parameters, the schema is fully covered, and the baseline for a parameterless tool is 4. The description adds no parameter-specific detail because there are none, but it does clarify the source (latest scan) indirectly.
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 uses the specific verb 'List' and identifies the exact resource: AI-generated content suggestions (FAQs, differentiators, use cases, about copy) from the latest scan. This clearly distinguishes it from sibling tools like generate_content or scan_visibility.
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 implies it should be used after a scan ('from my latest scan') and for the signed-in user, but it does not explicitly contrast with alternatives or state when not to use it. No sibling tool is mentioned, so guidance is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a unique aspect of brand visibility management: scanning, auditing, listing gaps, generating content, and retrieving scores. No two tools overlap in function.
All tool names follow a consistent verb_noun pattern using snake_case (e.g., scan_visibility, get_latest_score). This makes it easy for an agent to infer action and target.
With 5 tools, the server covers the core workflows of scanning, auditing, content gap analysis, content generation, and score retrieval without being bloated or sparse.
The tool surface provides a complete lifecycle for brand visibility analysis: scan → audit → identify gaps → generate content → get score. No obvious missing operations.