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TypeScript
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Read the visits that came from AI assistants

get_ai_traffic
Read-only

Measure AI-referred sessions from Google Analytics, including engagement and key events, to determine if AI assistant visibility converts into site visits.

Instructions

The sessions Google Analytics attributes to AI assistants for the active project's site: how many arrived, how engaged they were, and how many key events they triggered. This is the tool for 'is any of this visibility turning into visits'.

Pass by to rank the period instead of totalling it: source for the assistants that sent the visitors, page for the pages they land on. assistant narrows any of the three to one assistant. A ranking answers with the strongest entries rather than a list to walk to the end of.

Google's figures answer a different question from everything else here: visibility counts the answers that named the brand, and this counts the people who then arrived. Search Console covers ordinary Google results — ctr is a rate between 0 and 1, and position counts from 1, so lower is better. AI traffic is Google Analytics sessions whose referrer was recognised as an assistant, which undercounts by design: an assistant that names the brand without linking it sends nobody, and somebody who reads an answer and then types the domain arrives as direct traffic. Read a rise here as people acting on the answers, never as how often the brand is named. Mind the two senses of the phrase: the aiTraffic field on a prompt is the demand behind that question, while get_ai_traffic counts sessions that reached the site.

Both integrations are bound to the project in the PromptEye app. A project with nothing bound answers with zeros and empty lists, which reads exactly like a site nobody visits — so call get_google_status before reporting a zero as a finding, and say which of the two it was.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
byNoRank the period along this axis instead of reporting its totals.
limitNoHow many entries to return, at most 200. Ignored without `by`.
endDateNoLast day to report on, inclusive. Defaults to today, and must be within 366 days of startDate.
assistantNoOnly sessions from this AI assistant, matched without regard to case against the referrer: `openai` also matches chatgpt, `anthropic` matches claude, `google` matches gemini, and `microsoft` matches copilot and bing.
startDateNoFirst day to report on, inclusive. Defaults to 30 days before today.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.0.16

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description doesn't need to repeat those. However, it adds valuable behavioral context beyond annotations: it explains the undercounting by design (assistants without links, direct traffic), the distinction between visibility and sessions, and the zeros-with-no-binding behavior. It could mention pagination or return format, but the read-only and open-world safety is already covered, so a 4 is justified.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear paragraphs, front-loading the primary purpose and key parameters. It is slightly verbose in the alternative guidance section (Search Console details), but every sentence earns its place by clarifying important distinctions. It could be trimmed slightly without losing value, hence a 4 rather than 5.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (5 optional params, no output schema), the description fully compensates: it reveals the semantic difference from aiTraffic, explains zero-return cases and the prerequisite check, and even covers date range behavior implicitly through schema. It is complete for an agent to call it correctly, with no critical missing information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 100% schema coverage, the description adds substantial value: it explains the purpose of 'by' (rank vs total), that 'assistant' narrows any of the three, and gives key examples of matching (openai matches chatgpt). The schema does not explain the practical effects of `by` on the response shape or provide these aliases, so the description goes far beyond what the schema alone provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('read'), a resource ('visits from AI assistants'), and the exact metrics (count, engagement, key events), and clearly differentiates it from Google Search Console and other analytics. It even clarifies the two senses of 'aiTraffic' vs this tool, leaving no ambiguity for the agent.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly explains when to use it ('this is the tool for 'is any of this visibility turning into visits'') and contrasts with Google Search Console and other tools. It also instructs to call get_google_status before reporting a zero as a finding, providing clear context and exclusions.

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

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