Fetch the results of an AI-visibility scan started with scan_ai_visibility.
Args:
report_id: The id returned by scan_ai_visibility.
Returns:
While running: {status: "running", progress}. When done: the visibility
score, how often AI mentions the brand, share of voice, top competitors
winning the answers, and the highest-priority fixes, plus the report_url.
| Name | Required | Description | Default |
|---|---|---|---|
| report_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already set readOnlyHint=true and destructiveHint=false. The description adds behavioral context by describing two possible return states ('while running' vs 'when done'), which clarifies the polling aspect and output structure, going 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 reasonably concise with three sentences: one stating the purpose, and two structured as 'Args' and 'Returns' sections. It is front-loaded and avoids unnecessary words, though could be slightly tighter.
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 tool with one parameter, no output schema, and good annotations, the description covers the purpose, parameter origin, and complete return behavior (including polling states). It fully equips an agent to use the tool 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?
The description explains the single parameter 'report_id' as 'The id returned by scan_ai_visibility,' adding meaningful context beyond the schema (which has no description and 0% coverage). This informs the agent where the value comes from.
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 'Fetch the results of an AI-visibility scan started with scan_ai_visibility,' which specifies the verb (fetch), resource (results of AI-visibility scan), and explicitly names the sibling tool (scan_ai_visibility) to distinguish itself.
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 indicates that this tool should be used after scan_ai_visibility has provided a report_id, providing clear context for when to use it. It does not explicitly state when not to use it or list alternatives, but the linkage to the sibling tool is sufficient.
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.
The two tools have clearly distinct roles: one initiates a scan, the other retrieves results. No functional overlap or ambiguity.
Both names follow the same verb_noun pattern ('scan_visibility' and 'get_visibility_report'), consistent and predictable.
With only two tools, the set is minimal and focused, perfectly matching the simple scan-and-retrieve workflow without excess.
The tool pair covers the complete lifecycle of an AI-visibility scan: initiating it and fetching the resulting report. No missing steps are apparent.