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AI assistant traffic

get_ai_traffic

Read-only

Return AI-assistant (ChatGPT/Claude/Perplexity/Gemini/Copilot) traffic for the given period. mode='referred' (default) lists landing pages that received clicked AI traffic — per page × AI source: sessions, bounce rate (%, always computed; judge reliability via the sessions count), summed revenue, and last citation date (last_cited_at is JST ISO8601 with a +09:00 offset — the same basis as the dashboard, so dates line up when compared) (default limit 100); a view GA4/GSC cannot produce (GSC is Google-search only; GA4 lacks an AI-source breakdown). mode='gaps' returns where the site leaves AI value on the table as a ranked action list: (1) missed_citation_pages — content articles with real audience but ~0 AI traffic (push for AI citation / GEO), ranked by engagement-weighted reach; (2) under_monetized_ai_pages — pages WITH AI traffic engaging below the site's own AI norm (improve landing/CTA), ranked by AI arrivals lost below benchmark (default limit 10/list); methodology fixed in code. site_id is OPTIONAL when OAuth-authenticated. Default period is the last 30 days; pass period='today'/'7d'/'90d' or a raw day count (1-365). Scope is clicked citations only.

Input Schema

TableJSON Schema

Name	Required	Default
`mode`	No	referred
`limit`	No
`period`	No	30d
`site_id`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`mode`	Yes
`rows`	No
`basis`	Yes
`notes`	No
`period`	Yes
`site_id`	Yes
`criteria`	No
`ai_sources`	No
`assumptions`	No
`limitations`	No
`total_ai_sessions`	No
`missed_citation_pages`	No
`under_monetized_ai_pages`	No
`site_benchmark_engaged_rate`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedAug 17, 2026

removedOutput schema / properties / criteria / properties / citation_max_bounce
```
Removed value: -{
-  "type": "number"
-}
```

changedOutput schema / properties / criteria / required

Previous value: -[
-  "content_namespaces",
-  "min_pageviews",
-  "citation_max_bounce",
-  "min_ai_sessions"
-]New value: +[
+  "content_namespaces",
+  "min_pageviews",
+  "min_ai_sessions"
+]

Changed1 schema field changedJun 21, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": false,
+  "properties": {
+    "ai_sources": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "assumptions": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "basis": {
+      "type": "string"
+    },
+    "criteria": {
+      "additionalProperties": false,
+      "properties": {
+        "citation_max_bounce": {
+          "type": "number"
+        },
+        "content_namespaces": {
+          "items": {
+            "type": "string"
+          },
+          "type": "array"
+        },
+        "min_ai_sessions": {
+          "type": "number"
+        },
+        "min_pageviews": {
+          "type": "number"
+        }
+      },
+      "required": [
+        "content_namespaces",
+        "min_pageviews",
+        "citation_max_bounce",
+        "min_ai_sessions"
+      ],
+      "type": "object"
+    },
+    "limitations": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "missed_citation_pages": {
+      "items": {
+        "additionalProperties": false,
+        "properties": {
+          "ai_sessions": {
+            "type": "number"
+          },
+          "bounce_rate": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "gap_type": {
+            "const": "missed_citation",
+            "type": "string"
+          },
+          "impact_score": {
+            "type": "number"
+          },
+          "page": {
+            "type": "string"
+          },
+          "pageviews": {
+            "type": "number"
+          },
+          "reason": {
+            "type": "string"
+          }
+        },
+        "required": [
+          "page",
+          "pageviews",
+          "bounce_rate",
+          "ai_sessions",
+          "gap_type",
+          "reason",
+          "impact_score"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "mode": {
+      "enum": [
+        "referred",
+        "gaps"
+      ],
+      "type": "string"
+    },
+    "notes": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "period": {
+      "additionalProperties": false,
+      "properties": {
+        "days": {
+          "type": "number"
+        },
+        "end": {
+          "type": "string"
+        },
+        "start": {
+          "type": "string"
+        }
+      },
+      "required": [
+        "start",
+        "end",
+        "days"
+      ],
+      "type": "object"
+    },
+    "rows": {
+      "items": {
+        "additionalProperties": false,
+        "properties": {
+          "ai_source": {
+            "type": "string"
+          },
+          "bounce_rate": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "last_cited_at": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "page": {
+            "type": "string"
+          },
+          "revenue_jpy": {
+            "type": "number"
+          },
+          "sessions": {
+            "type": "number"
+          }
+        },
+        "required": [
+          "page",
+          "ai_source",
+          "sessions",
+          "bounce_rate",
+          "revenue_jpy",
+          "last_cited_at"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "site_benchmark_engaged_rate": {
+      "type": "number"
+    },
+    "site_id": {
+      "type": "string"
+    },
+    "total_ai_sessions": {
+      "type": "number"
+    },
+    "under_monetized_ai_pages": {
+      "items": {
+        "additionalProperties": false,
+        "properties": {
+          "ai_engaged": {
+            "type": "number"
+          },
+          "ai_revenue_jpy": {
+            "type": "number"
+          },
+          "ai_sessions": {
+            "type": "number"
+          },
+          "engaged_rate": {
+            "type": "number"
+          },
+          "gap_type": {
+            "const": "under_monetized",
+            "type": "string"
+          },
+          "impact_score": {
+            "type": "number"
+          },
+          "page": {
+            "type": "string"
+          },
+          "reason": {
+            "type": "string"
+          },
+          "site_benchmark_rate": {
+            "type": "number"
+          }
+        },
+        "required": [
+          "page",
+          "ai_sessions",
+          "ai_engaged",
+          "engaged_rate",
+          "site_benchmark_rate",
+          "ai_revenue_jpy",
+          "gap_type",
+          "reason",
+          "impact_score"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "site_id",
+    "mode",
+    "period",
+    "basis"
+  ],
+  "type": "object"
+}

AddedJun 3, 2026

TDQS

A4.7/5.0

Behavior5/5

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

The description discloses all relevant behavioral traits: return fields (sessions, bounce rate with reliability caveat, revenue, last_cited_at with timezone), default limits per mode, scope (clicked citations only), and that methodology is fixed in code. The readOnlyHint annotation is consistent with the read-only nature described. No contradictions.

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 front-loaded with the primary purpose and then details each mode. It is dense but efficient; every sentence adds value. A slight reduction in length or use of bullet points could improve scannability, but it remains well-structured and avoids fluff.

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 (two modes, four parameters, output schema present), the description covers all necessary aspects: input parameters, return values for each mode, methodology notes, timezone info, and scope. The presence of an output schema does not reduce the need for explanation, and the description exceeds expectations.

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?

With 0% schema description coverage, the description fully compensates by explaining each parameter: mode (enum values and defaults), limit (defaults per mode), period (string shortcuts and integer range), and site_id (optional when OAuth). It also explains the format of the last_cited_at field in the output, aiding understanding of the data.

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 clearly states the tool returns AI-assistant traffic for a given period, with two distinct modes ('referred' and 'gaps') that are each explained in detail. It lists the specific AI sources (ChatGPT, Claude, etc.) and contrasts with GA4/GSC, making the purpose unambiguous and distinguished from general analytics tools.

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

Usage Guidelines4/5

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

The description explains when to use each mode: 'referred' for listing landing pages with AI traffic and 'gaps' for identifying missed opportunities. It also notes that the view cannot be produced by GA4/GSC, implying it's the only tool for this purpose. However, it does not explicitly compare to sibling tools like get_breakdown or get_keyword_performance, leaving some ambiguity about alternatives.

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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