Mencoro

AI mention sentiment breakdown

get_sentiment_breakdown

Read-onlyIdempotent

Positive/neutral/negative sentiment split of the brand's AI mentions over a date window, per AI engine and per competitor. Use for tone/sentiment questions. For the brand's own mention counts by type, tone and qualifier use get_mention_mix; to read the actual mention texts use get_mention_samples. Dates must fall within the data retention window. Answers questions like "is anything negative being said about my brand" or "how positive is my coverage".

Input 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
`queryClusterIds`	No	restrict to these keyword clusters
`includeUngroupedQueries`	No	Sent alone, restricts to tracked queries that belong to no cluster (refused if there are none). Sent with queryClusterIds, adds them to those clusters. Omit both for all tracked queries.

Output Schema

Name	Required	Description	Default
`perEngine`	Yes
`perCompetitor`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 16, 2026

addedInput schema / properties / includeUngroupedQueries / description

Added value: +"Sent alone, restricts to tracked queries that belong to no cluster (refused if there are none). Sent with queryClusterIds, adds them to those clusters. Omit both for all tracked queries."

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"
+]

Changed4 schema fields changedSep 17, 2026
- addedOutput schema / properties / perCompetitor / items / properties / positivityIndex / anyOf
```
Added value: +[
+  {
+    "type": "number"
+  },
+  {
+    "type": "null"
+  }
+]
```
- removedOutput schema / properties / perCompetitor / items / properties / positivityIndex / type
```
Removed value: -[
-  "number",
-  "null"
-]
```
- addedOutput schema / properties / perEngine / items / properties / positivityIndex / anyOf
```
Added value: +[
+  {
+    "type": "number"
+  },
+  {
+    "type": "null"
+  }
+]
```
- removedOutput schema / properties / perEngine / items / properties / positivityIndex / type
```
Removed value: -[
-  "number",
-  "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 readOnlyHint, idempotentHint, destructiveHint=false and a closed world, so safety is covered. The description adds genuinely useful context beyond that: the breakdown dimensions returned and the data-retention constraint on dates, which affects call validity.

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 the core purpose, then routing, then the retention caveat; the illustrative questions ('is anything negative being said about my brand') are slightly redundant but aid intent matching. No sentence is wasted, though the description is a touch longer than strictly needed.

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 need not be explained, and the description covers purpose, alternatives, and the retention constraint. It leaves the filtering semantics (countries, query clusters) to the schema, which is acceptable but not complete for an 8-parameter tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 50% across 8 params; the description adds little parameter-level meaning beyond reiterating the date window (already in schema) and gesturing at engines via 'per AI engine'. Countries, queryClusterIds, and includeUngroupedQueries semantics live only in the schema, so the description does not compensate for the coverage gap.

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+resource ('positive/neutral/negative sentiment split of the brand's AI mentions over a date window') plus the grouping dimensions (per AI engine, per competitor). It clearly distinguishes itself from get_mention_mix and get_mention_samples, so an agent can route without opening schemas.

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?

Gives explicit when-to-use guidance ('Use for tone/sentiment questions') and names two alternatives with the exact condition that selects each ('brand's own mention counts by type... use get_mention_mix', 'read the actual mention texts... use get_mention_samples'). It also flags the retention-window prerequisite for the date parameters.

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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