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ai_mentions

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Find where a keyword appears in ChatGPT and Google AI outputs, returning AI search volume, total mentions, and detailed snippets with sources. Results are country-level; verify location_used.

Instructions

Use when finding where a keyword appears in ChatGPT and Google AI outputs. Returns ai_search_volume, total_mentions, and mention details with question, answer_snippet, sources_cited, and platform. IMPORTANT: results are country-level (location_scope: "country") regardless of the location passed — a city resolves to its country and the numbers cover all of it. Check location_used. For city-level AI results use ai_scraper or ai_mode. Costs 88 credits.

Input Schema

Name	Required	Description
`limit`	No	Max mentions. Default: 10, max: 100
`keyword`	Yes	Keyword to search for (e.g. "best plumber in Buffalo")
`language`	No	Language as ISO 639-1 code or name (e.g. "fr", "French"). Defaults to English wherever we hold English AI data for that country, and to the largest database that country has otherwise.
`location`	No	Location for results (e.g. "Portland, OR"). Default: US
`platforms`	No	Platforms to query. Default: all

Output Schema

Name	Required	Description	Default
`language`	No
`mentions`	Yes
`location_used`	No
`location_scope`	No
`total_mentions`	Yes
`ai_search_volume`	Yes
`platforms_available`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv1.1.0Oct 22, 2026

addedInput schema / properties / language

Added value: +{
+  "description": "Language as ISO 639-1 code or name (e.g. \"fr\", \"French\"). Defaults to English wherever we hold English AI data for that country, and to the largest database that country has otherwise.",
+  "type": "string"
+}

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": true,
+  "properties": {
+    "ai_search_volume": {
+      "type": "number"
+    },
+    "language": {
+      "type": "string"
+    },
+    "location_scope": {
+      "enum": [
+        "local",
+        "country"
+      ],
+      "type": "string"
+    },
+    "location_used": {
+      "type": "string"
+    },
+    "mentions": {
+      "items": {
+        "additionalProperties": true,
+        "properties": {
+          "answer_snippet": {
+            "type": "string"
+          },
+          "date": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "platform": {
+            "type": "string"
+          },
+          "question": {
+            "type": "string"
+          },
+          "sources_cited": {
+            "items": {
+              "type": "string"
+            },
+            "type": "array"
+          }
+        },
+        "required": [
+          "question",
+          "answer_snippet",
+          "sources_cited",
+          "platform",
+          "date"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "platforms_available": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "total_mentions": {
+      "type": "number"
+    }
+  },
+  "required": [
+    "ai_search_volume",
+    "total_mentions",
+    "mentions"
+  ],
+  "type": "object"
+}

First observedv0.1.0May 11, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already declare readOnly/openWorld/non-destructive, so safety is covered; the description goes further with behavior the annotations cannot express — results are country-level regardless of the location passed, the returned location_used should be verified, and each call costs 88 credits. That is exactly the kind of hidden trait an agent needs before calling.

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?

Tight and front-loaded: purpose, then return shape, then the IMPORTANT caveat, then the alternative, then cost. The mid-sentence enumeration of return fields (question, answer_snippet, sources_cited, platform) is partly redundant given an output schema exists, but it is compact enough to earn 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?

For a 5-parameter, read-only, open-world tool with a full output schema and full annotation coverage, nothing an agent needs is missing: purpose, scope degradation caveat, alternative routing, and cost are all stated. Output schema handles return values, so the description need not expand further.

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. The description adds real semantics beyond the schema: it warns that the 'location' parameter is silently degraded to country scope even for a city input, which directly contradicts what the schema example ('Portland, OR') implies. Other parameters are left to the schema, which is adequate.

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 first sentence gives a specific verb ('finding where a keyword appears') and a specific resource scope ('ChatGPT and Google AI outputs'), and the return fields (ai_search_volume, total_mentions, mention details) pin down the output shape. An agent can distinguish this from ai_visibility or brand_mentions without opening the 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?

Explicitly states the condition for use ('finding where a keyword appears') and names the alternatives with their own condition: 'For city-level AI results use ai_scraper or ai_mode.' It also flags the 88-credit cost, which lets an agent make a cost/benefit call before invoking.

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