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

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
`queryClusterIds`	No	restrict to these keyword clusters
`includeUngroupedQueries`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`perEngine`	Yes
`perCompetitor`	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"
+]

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

A4.1/5.0

Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered. The description adds a genuinely useful behavioral constraint: dates must fall within the data retention window. It also discloses granularity (per engine, per competitor), though it says nothing about pagination or result size.

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-loaded with the core capability, then routing, then constraints. Slightly padded by the two example questions that restate the tone/sentiment guidance, but overall tight and purposeful.

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

Completeness3/5

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 no explanation, and annotations cover safety. However, for an 8-parameter tool with 38% schema coverage, several parameters (cluster IDs, ungrouped inclusion, countries) are never addressed, leaving gaps an agent must guess at.

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

Parameters2/5

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

Schema coverage is only 38%, so the description must compensate, and it largely does not. It implies date-window and engine/competitor scoping but never explains queryClusterIds, includeUngroupedQueries, or how countries interact with the breakdown. The undocumented parameters remain opaque.

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 AI mentions, scoped by date window, AI engine and competitor. It explicitly 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 (tone/sentiment questions) plus two named alternatives with the conditions that select them. Example questions ('is anything negative being said', 'how positive is my coverage') anchor the intent concretely.

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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