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brand_mentions

Read-only

Monitor where a brand is mentioned online and analyze sentiment, returning total mentions, positive/neutral/negative breakdowns, and mention details with domain authority and sentiment.

Instructions

Use when monitoring where a brand is mentioned online with sentiment analysis. Returns total_mentions, sentiment_breakdown (positive/neutral/negative), and mention details with domain_authority and sentiment. Costs 23 credits.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "business_name": "<business_name>"
    },
    "name": "brand_mentions"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Max mentions. Default: 20, max: 100
`business_name`	Yes	Business or brand name to search for

Output Schema

Name	Required	Description	Default
`mentions`	Yes
`total_mentions`	Yes
`sentiment_breakdown`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv1.1.0Oct 22, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": true,
+  "properties": {
+    "mentions": {
+      "items": {
+        "additionalProperties": true,
+        "properties": {
+          "date": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "domain": {
+            "type": "string"
+          },
+          "domain_authority": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "sentiment": {
+            "enum": [
+              "positive",
+              "neutral",
+              "negative"
+            ],
+            "type": "string"
+          },
+          "snippet": {
+            "type": "string"
+          },
+          "title": {
+            "type": "string"
+          },
+          "url": {
+            "type": "string"
+          }
+        },
+        "required": [
+          "title",
+          "url",
+          "domain",
+          "domain_authority",
+          "sentiment",
+          "snippet",
+          "date"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "sentiment_breakdown": {
+      "additionalProperties": true,
+      "properties": {
+        "negative": {
+          "type": "number"
+        },
+        "neutral": {
+          "type": "number"
+        },
+        "positive": {
+          "type": "number"
+        }
+      },
+      "required": [
+        "positive",
+        "neutral",
+        "negative"
+      ],
+      "type": "object"
+    },
+    "total_mentions": {
+      "type": "number"
+    }
+  },
+  "required": [
+    "total_mentions",
+    "sentiment_breakdown",
+    "mentions"
+  ],
+  "type": "object"
+}

First observedv0.1.0May 11, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already declare read-only and open-world access, so the description's main contribution is the 23-credit cost, a genuinely useful operational detail absent from structured fields. Return contents are summarized but largely duplicated by the output schema, and nothing is said about pagination or coverage limits for the search.

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?

Three tight sentences: trigger first, output shape second, cost last. Nothing is padded and the most actionable line is front-loaded.

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?

For a two-parameter read-only tool with an output schema and full annotations, the description covers purpose, outputs, and cost adequately. The remaining gap is sibling disambiguation against ai_mentions, which matters in a toolset this dense.

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 coverage is 100%, fully documenting business_name and the limit range/default. The description adds no parameter meaning beyond that, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('monitoring where a brand is mentioned online') plus the differentiating capability of sentiment analysis. It does not distinguish itself from the sibling ai_mentions, so an agent must infer which mention-tracking tool applies.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

'Use when monitoring where a brand is mentioned online' gives an implied context for use, but there are no exclusions, prerequisites, or named alternatives (notably ai_mentions or reputation_audit).

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