get_ai_trafficVisitors the AI engines actually sent to the business's site in the last 30 days, recorded by the site's own beacon: totals by engine and by week, with the tracking wiring status. This closes the loop from being named in answers to humans arriving.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool readOnlyHint=true, and the description adds behavior-relevant context: data comes from the site's own beacon, is limited to 30 days, and includes tracking wiring status so the agent knows the data may reflect installation state. It does not overpromise or imply side effects.
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?
Two well-organized sentences deliver the core scope first, then the higher-level purpose. Every phrase earns its place, and the 'closes the loop' framing adds context without padding.
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 no-parameter, read-only statistics tool, the description is largely complete: it names the time window, aggregation dimensions, source mechanism, and tracking status. It does not enumerate possible values of tracking status or edge cases like no beacon data, but the core calling decision is fully supported.
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?
There are zero parameters, so the description carries no parameter burden. It still adds value by clarifying what data the tool returns (totals by engine/week and tracking status), which would be the only meaningful semantic content for a no-input tool.
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 states a specific verb-resource relationship: it returns AI-sent visitors to the site over a fixed 30-day window, broken down by engine and week. It also distinguishes itself from siblings like get_mentions or get_visibility by emphasizing 'AI engines actually sent' and 'recorded by the site's own beacon.'
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 gives clear usage context: use this tool when you need the direct traffic driven by AI engines, with attribution to engine and week. It does not explicitly name alternative sibling tools or state when not to use it, but the focus on 'visitors the AI engines sent' and 'tracking wiring status' makes the intended use case unmistakable.
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.
Most tools have clearly distinct objects: answers, trajectories, citations, mentions, traffic, scores, and briefs. A few pairs like get_citation_sources vs get_source_profile and get_rivals vs get_share_of_voice overlap thematically, but their descriptions clarify different granularity and purpose.
All 16 tools follow an identical get_ + snake_case noun phrase pattern, such as get_agent_view, get_share_of_voice, and get_question_trajectories. This makes the tool set highly predictable and easy to navigate.
At 16 tools, the set sits just above the ideal 3-15 range, but each tool addresses a distinct facet of AI visibility monitoring. The count feels justified rather than bloated, though it is slightly heavy for a read-only monitor.
The suite covers the full read-only monitoring lifecycle: visibility scores, raw answers, question histories, competitor comparisons, cited sources, mentions, traffic, benchmarks, action plans, personas, and content briefs. There are no obvious dead ends or missing core operations for the stated purpose.