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TypeScript
Remote

get_ai_visibility_history

Read-only

Retrieve a paginated history of scored AI Visibility checks, showing how ChatGPT, Claude, and Gemini rank brands per prompt, with scores and competitor rankings.

Instructions

A page of scored AI Visibility checks. Uses checkId, not runId: a check is one full cycle, every prompt against every AI model that check asked (5 today; older checks keep the smaller set they ran with).

  • Only scored checks are listed. Under the full-coverage gate a cycle that came back short is never scored and does not appear. Checks published before that gate can have been scored over fewer answers than queries asked, and queries sent is not returned, so never call a listed check fully covered.

  • score is WHERE a brand lands when named (top 5 positions only), never who is ahead: a standing claim ('you lead', 'the leader is X') rests on mentionRate, never on score. A 0 score with a non-zero mentionRate means named, below the top 5; a 0 rate means no counted answer named it.

  • summary.promptMarket, where present, says whether the monitored questions reach the market the tracked competitor list describes. Report explanation.text verbatim. An absent promptMarket is a reading that could not be produced, never a pass. Never say the prompts are wrong: the reading compares two things the customer supplied and cannot say which is off.

  • truncated true means the page hit a size cap and whole entries were dropped from the end: lower limit to see the rest. pagination.hasMore means another page. Per row, summary.totalEntries (brand entries the check recorded) predicts how large that check's get_ai_visibility_check_detail payload is: read it before asking for raw answers. summary.customer.perPrompt (label, the models that named the customer, a 0-100 score) answers 'which prompt am I losing on' without another call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number (1-indexed, default: 1)
viewNocompact or full; omit for the server's default view. compact trims each check: the first 10 competitor rankings plus the tracked competitors and the customer, and promptMarket without perPrompt (about 3,600 characters per check). full returns every ranking and perPrompt (about 12,500 per check). page and limit work in both.
limitNoItems per page (default: 20, max: 100). When the response says truncated: true, the page hit a size cap and whole entries were dropped from the end; lower limit to see them.
projectIdYesProject ID (from list_projects)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv4.0.1
    • removedInput schema / additionalProperties
      Removed value: -false
    • changedInput schema / properties / limit / description
      Previous value: -"Items per page (default: 20, max: 100)"New value: +"Items per page (default: 20, max: 100). When the response says truncated: true, the page hit a size cap and whole entries were dropped from the end; lower limit to see them."
    • addedInput schema / properties / page / maximum
      Added value: +9007199254740991
    • addedInput schema / properties / view
      Added value: +{
+  "description": "compact or full; omit for the server's default view. compact trims each check: the first 10 competitor rankings plus the tracked competitors and the customer, and promptMarket without perPrompt (about 3,600 characters per check). full returns every ranking and perPrompt (about 12,500 per check). page and limit work in both.",
+  "enum": [
+    "compact",
+    "full"
+  ],
+  "type": "string"
+}
  2. First observedv1.0.0

TDQS

A4.4/5.0
Behavior5/5

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

Annotations only cover the safety profile (readOnlyHint, openWorldHint=false), so the description carries the rest and does: only scored checks appear, checks published before the coverage gate may be scored over fewer answers than asked, absent promptMarket is an unproducible reading rather than a pass, and truncated true means whole entries were dropped from the end (lower limit). These are exactly the non-obvious behaviors an agent would otherwise get wrong.

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 is front-loaded in sentence one and the rest is broken into scannable bullets, each covering a distinct decision (score vs mentionRate, promptMarket, truncated, pagination, totalEntries). It is dense and longer than ideal, and some row-field semantics arguably belong to the detail/trend tools, but nearly every sentence earns its place.

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?

There is no output schema, so the description must explain the return payload, and it does thoroughly: score/mentionRate meaning, per-row summary fields, promptMarket, truncation and pagination signals. An agent has everything needed to call and interpret this tool correctly.

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

Parameters3/5

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

Schema description coverage is 100%, so page, view, limit and projectId are already documented in the schema, including the compact/full character budgets and the truncated/limit interplay. The prose adds framing (why to lower limit) but no parameter semantics beyond what the schema states, so the baseline 3 applies.

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

Purpose4/5

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

The opening states a concrete resource ("a page of scored AI Visibility checks") and immediately disambiguates the identifier model ("uses checkId, not runId"), which tells an agent this is a listing/history tool rather than the run-oriented siblings. It stops short of a clean verb and doesn't name the sibling endpoints (get_ai_visibility_check_detail, get_ai_visibility_trend) it competes with, so differentiation relies on inference.

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 routes the agent explicitly: read summary.totalEntries before pulling the heavier get_ai_visibility_check_detail payload, and use summary.customer.perPrompt to answer "which prompt am I losing on" without another call. It also gives exclusion-style rules (never call a listed check fully covered; never say the prompts are wrong), leaving little to inference.

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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