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Read AI traffic insights

read_ai_traffic_insights
Read-onlyIdempotent

Read the patterns CiteLadder found by joining AI crawler, referral and citation data for one saved window: pages crawled but never visited from AI, pages getting AI referrals but not crawled recently, crawler errors on valuable pages, and key events concentrated on few pages. Each pattern has a short explanation, the numbers behind it and the pages involved, with coverage. These are co-occurrence, not cause. It reads the newest saved 30-day window by default; custom dates must match a saved window exactly. unavailable means no insight snapshot exists yet, and coverage.notice says why, such as GA4 not being connected. Use read_ai_traffic_pages for the per-page numbers.

Input Schema

NameRequiredDescriptionDefault
rangeNoA preset window ending today; or pass start_date and end_date instead.
end_dateNoLast day of a custom window, inclusive.
project_idYesA project id that list_projects returned.
start_dateNoFirst day of a custom window, inclusive.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • addedInput schema / properties / end_date / description
      Added value: +"Last day of a custom window, inclusive."
    • addedInput schema / properties / project_id / description
      Added value: +"A project id that list_projects returned."
    • addedInput schema / properties / range / description
      Added value: +"A preset window ending today; or pass start_date and end_date instead."
    • addedInput schema / properties / start_date / description
      Added value: +"First day of a custom window, inclusive."
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive/closed-world, and the description still adds substantial behavioral context: default window selection, the exact-match constraint on custom dates, the meaning of the 'unavailable' state, that coverage.notice explains the cause (e.g. GA4 not connected), and the 'co-occurrence, not cause' interpretation caveat.

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?

Purpose and return shape are front-loaded, and each sentence carries distinct information (patterns, return content, window rules, failure state, sibling routing). It is dense rather than padded, though the single long paragraph is heavier than strictly needed.

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?

With no output schema, the description compensates by describing what each pattern contains (explanation, numbers, pages, coverage) and how the empty/unavailable state plus coverage.notice should be interpreted. An agent has everything required to call and interpret the result.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds a constraint not present in the schema: custom start_date/end_date must match a saved window exactly, and range resolves to the newest saved 30-day window. That extra semantic layer slightly exceeds the schema-documented parameter text.

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?

States a specific verb and resource ('Read the patterns CiteLadder found by joining AI crawler, referral and citation data for one saved window') and enumerates the exact pattern types returned. It also explicitly differentiates itself from the sibling read_ai_traffic_pages, so an agent can route between them without opening either schema.

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?

Names the alternative ('Use read_ai_traffic_pages for the per-page numbers') and states the selection condition, plus explicit window rules: newest saved 30-day window by default, custom dates must match a saved window exactly. Both when-to-use and when-not-to-use are covered.

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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