get_visibility_checkCollect the result of a measurement started with start_visibility_check. Returns status running while the providers are still answering, so poll every 20 to 30 seconds. When ready it reports a verdict, how many of the buyer questions the brand won, which competitors were named instead, and which providers actually answered.
| Name | Required | Description | Default |
|---|---|---|---|
| runId | Yes | From start_visibility_check |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnly/idempotent annotations, the description discloses important behavior: it can return a 'running' status, it requires polling, and it details what the ready result contains (verdict, won counts, competitors, providers). This gives the agent a reliable model of the tool's dynamic behavior.
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 no wasted words: purpose, polling behavior, and ready-state output are each addressed succinctly. The most important usage constraint is front-loaded.
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 polling tool with rich annotations, the description covers the full call cycle: when to call, how long to wait, what status to expect, and what the final result contains. No output schema exists, but the description adequately compensates by listing the result components.
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 input schema already documents runId with 100% coverage ('From start_visibility_check'), and the description reinforces the relationship without adding deep parameter-level detail. Baseline 3 is appropriate because the schema carries the parameter semantics.
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 identifies the action as collecting the result of a measurement started with start_visibility_check, and it naturally distinguishes this tool from its sibling, which initiates the measurement. The verb 'collect' plus the specific resource make the purpose unambiguous.
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: it must be called after start_visibility_check, and it should be polled every 20 to 30 seconds while status is running. It does not explicitly list exclusions or alternatives, but the single sibling relationship is clear enough.
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.
start_visibility_check initiates the measurement and returns a runId, while get_visibility_check consumes that runId to fetch the result. Their roles are completely distinct and easy for an agent to separate.
Both tools follow a clear verb_noun pattern, sharing the same visibility_check noun and differing only in the start/get action. This is highly consistent and predictable.
Two tools is minimal, but it fits the server's narrow start-and-retrieve workflow perfectly. No extra tools are needed for this domain.
The tool surface covers the full lifecycle of a visibility measurement: launch the check, retrieve the verdict, and handle quota scenarios through the response. There are no obvious dead ends for the stated purpose.