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ai_top_pages

Read-only

Identifies the specific web pages AI models cite for a keyword, returning URLs, domains, mention counts, and AI search volume at country level so you can target pages rather than just domains.

Instructions

Use when finding specific pages (not just domains) that AI models reference most. Returns top_pages with url, domain, mentions, and ai_search_volume. More granular than ai_top_sources. 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 81 credits.

Input Schema

Name	Required	Description
`limit`	No	Max pages. Default: 10, max: 50
`keyword`	Yes	Keyword to search for (e.g. "best dentist")
`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. "Chicago, IL"). Default: US
`platforms`	No	Platforms to query. Default: all

Output Schema

Name	Required	Description	Default
`keyword`	Yes
`language`	No
`top_pages`	Yes
`location_used`	No
`location_scope`	No
`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": {
+    "keyword": {
+      "type": "string"
+    },
+    "language": {
+      "type": "string"
+    },
+    "location_scope": {
+      "enum": [
+        "local",
+        "country"
+      ],
+      "type": "string"
+    },
+    "location_used": {
+      "type": "string"
+    },
+    "platforms_available": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "top_pages": {
+      "items": {
+        "additionalProperties": true,
+        "properties": {
+          "ai_search_volume": {
+            "type": "number"
+          },
+          "domain": {
+            "type": "string"
+          },
+          "mentions": {
+            "type": "number"
+          },
+          "url": {
+            "type": "string"
+          }
+        },
+        "required": [
+          "url",
+          "domain",
+          "mentions",
+          "ai_search_volume"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "keyword",
+    "top_pages"
+  ],
+  "type": "object"
+}

First observedv0.1.0May 11, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Beyond the read-only annotations, the description discloses critical behavior: results are country-level regardless of the location passed, city inputs resolve to country scope, location_used should be checked, and the call costs 81 credits. These are exactly the operational details an agent needs before invoking it.

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

Conciseness5/5

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

The description is dense but front-loaded: purpose first, return fields second, the country-level caveat third, alternatives fourth, and cost last. Every sentence contributes actionable information.

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 read-only, open-world lookup tool with full schema descriptions and an output schema, the description covers the remaining decision-critical context: granularity, country-level behavior, alternatives, and cost. Nothing important for correct invocation appears missing.

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 important meaning about location semantics that the schema does not capture: location is effectively country-level and city values are resolved upward. It does not add much beyond the schema for limit, language, or platforms, but the location caveat is significant.

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 resource and scope: finding specific pages that AI models reference most, not just domains. It explicitly distinguishes the tool from ai_top_sources as more granular, so an agent can select it 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?

It gives an explicit use case and names alternatives for the key edge case: city-level AI results should use ai_scraper or ai_mode. It also makes clear that this tool is the page-level counterpart to ai_top_sources.

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