Mencoro

Cited domains and pages

get_cited_sources

Read-onlyIdempotent

The domains (groupBy=domain) or pages (groupBy=page) most cited across a project's AI answers in a date window — the sources the answer engines drew on. Per source: how many times it was cited, how many distinct answers and tracked queries it appeared in, and its average rank within the citation lists. The list is UNFILTERED by ownership: it includes the brand's, competitors' and third-party sources. Only AI answer engines (chatgpt, claude, perplexity, google_ai_overview, google_ai_mode) produce citations. Dates must fall within the data retention window. Answers questions like "which websites and pages does the AI cite or quote for me versus competitors".

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No
`dateTo`	Yes
`offset`	No
`engines`	No	allowed values: chatgpt, claude, perplexity, google_ai_overview, google_ai_mode (only AI engines carry citations)
`groupBy`	No	"domain" to roll up by host, "page" to roll up by exact URL	domain
`dateFrom`	Yes
`projectId`	Yes
`organizationId`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`total`	Yes
`sources`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedOct 1, 2026

changedInput schema / properties / engines / description

Previous value: -"allowed values: chatgpt, perplexity, google_ai_overview, google_ai_mode (only AI engines carry citations)"New value: +"allowed values: chatgpt, claude, perplexity, google_ai_overview, google_ai_mode (only AI engines carry citations)"

changedInput schema / properties / engines / items / enum

Previous value: -[
-  "chatgpt",
-  "perplexity",
-  "google_ai_overview",
-  "google_ai_mode",
-  "google_serp",
-  "google_shopping"
-]New value: +[
+  "chatgpt",
+  "claude",
+  "perplexity",
+  "google_ai_overview",
+  "google_ai_mode",
+  "google_serp",
+  "google_shopping"
+]

Changed6 schema fields changedSep 17, 2026
- addedOutput schema / properties / sources / items / properties / avgPosition / anyOf
```
Added value: +[
+  {
+    "type": "number"
+  },
+  {
+    "type": "null"
+  }
+]
```
- removedOutput schema / properties / sources / items / properties / avgPosition / type
```
Removed value: -[
-  "number",
-  "null"
-]
```
- addedOutput schema / properties / sources / items / properties / domain / anyOf
```
Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]
```
- removedOutput schema / properties / sources / items / properties / domain / type
```
Removed value: -[
-  "string",
-  "null"
-]
```
- addedOutput schema / properties / sources / items / properties / sampleTitle / anyOf
```
Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]
```
- removedOutput schema / properties / sources / items / properties / sampleTitle / type
```
Removed value: -[
-  "string",
-  "null"
-]
```
First observedSep 15, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description adds genuinely useful behavior beyond the schema: the list is UNFILTERED by ownership (includes competitors and third parties) and only AI engines carry citations. It also describes the returned per-source metrics, which is extra context.

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?

Dense and front-loaded: the groupBy distinction and ownership caveat come early, followed by return metrics and constraints. Slightly long, but nearly every clause carries information an agent needs; no filler sentences.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, return values needn't be explained, yet the description still outlines the per-source metrics, and it adds the retention-window and AI-engine constraints. Combined with the annotations, an agent has enough to call it correctly; minor gaps remain around pagination (limit/offset).

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 description coverage is only 25%, so the description must compensate. It does explain the enum that matters most — groupBy=domain vs page (host vs exact URL) — and constrains engines to AI-only engines. It leaves limit/offset/defaults unaddressed, but those are intuitive and low-risk.

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 (get/list) and resource (cited domains/pages) with scope (most cited across a project's AI answers in a date window). The distinction between groupBy=domain and groupBy=page is front-loaded, so an agent can tell this apart from sibling analytics tools immediately.

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?

Gives clear context: the date window must fall within the retention window, only AI answer engines produce citations, and it answers 'which sites/pages does the AI cite for me vs competitors'. However, it names no alternative sibling (e.g. list_ai_responses or get_mention_mix) and offers no explicit when-not-to-use guidance.

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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