Mencoro

AI mention mix (type/tone/qualifier)

get_mention_mix

Read-onlyIdempotent

The project brand's own AI text-mention counts over a date window grouped by type, tone and qualifier; competitors (tracked or untracked) and unrelated brands are excluded. Counts are raw per-pass rows and leave out cited links, so they show mention composition, not the exact share-of-voice inputs (share of voice averages each check over its passes and also weights links). Use to understand mention composition. For the positive/neutral/negative sentiment split use get_sentiment_breakdown; to read the actual mention texts use get_mention_samples. Dates must fall within the data retention window. Answers questions like "am I recommended or just listed" or "break my mentions down by type".

Input Schema

TableJSON Schema

Name	Required	Description
`dateTo`	Yes
`engines`	No	allowed values: chatgpt, claude, perplexity, google_ai_overview, google_ai_mode, google_serp, google_shopping
`dateFrom`	Yes
`countries`	No	ISO-3166 alpha-2 country codes (e.g. "US", "GB", "DE"); a project's configured codes are listed by get_available_filters
`projectId`	Yes
`organizationId`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`byTone`	Yes
`byType`	Yes
`byQualifier`	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, google_serp, google_shopping"New value: +"allowed values: chatgpt, claude, perplexity, google_ai_overview, google_ai_mode, google_serp, google_shopping"

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"
+]

First observedSep 15, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Goes well beyond the annotations by disclosing that counts are raw per-pass rows, exclude cited links, and therefore differ from share-of-voice inputs (which average each check over passes and weight links). That is exactly the kind of non-obvious behavioral caveat annotations cannot express.

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?

Front-loads scope and the critical raw-count caveat, then routes to alternatives, then examples. A bit dense but every sentence carries routing or caveat value; slightly long for a 5.

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?

An output schema exists so return values need not be explained. The description still supplies the calculation caveat, scope exclusions, the retention constraint, sibling routing, and example questions – complete for a read-only aggregation tool.

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 33%, so the description must compensate. It adds the date retention-window constraint (applies to dateFrom/dateTo) and clarifies that grouping is fixed to type/tone/qualifier. It does not add semantics for engines or countries beyond what the schema already documents, so not a 5.

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?

Specific verb+resource+grouping: the project brand's own AI text-mention counts over a date window grouped by type, tone, and qualifier, with explicit scope (own brand only; competitors and unrelated brands excluded). It distinguishes itself from get_sentiment_breakdown and get_mention_samples by naming both siblings.

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?

Explicit routing: use this for mention composition, use get_sentiment_breakdown for the positive/neutral/negative split, use get_mention_samples for actual texts. Also gives a retention-window constraint and example questions. Nothing left 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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