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List Brand Analytics Tool

list-brand-analytics-tool

Read-onlyIdempotent

Per-brand aggregates over the filter window: visibility, share-of-voice, average sentiment, and average position. Returns one row for every tracked brand in scope; brands with no mentions in the window appear with zero visibility and share-of-voice and a null sentiment and position. Paginated.

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": {
      "project_id": "<project_id>",
      "start_date": "<start_date>",
      "end_date": "<end_date>"
    },
    "name": "list-brand-analytics-tool"
  }
}

Input Schema

Name	Required	Description
`page`	No	1-indexed page number. Defaults to 1.
`tags`	No	Tag IDs (prompt classifiers) to limit responses by. Optional.
`sort_by`	No	Sort key. One of: visibility, share_of_voice, sentiment, position. Defaults to visibility.
`end_date`	Yes	Inclusive UTC end of the analytics window. Must be on or after `start_date` and on or before today.
`interval`	No	Bucket granularity for time-series tools. One of: day, week, month, year. Required for time-series tools, ignored for aggregate tools.
`monitors`	No	Monitor IDs in the active project. Optional.
`per_page`	No	Page size, 1-100. Defaults to 25.
`segments`	No	Segment IDs (brand groupings) to slice analytics by. Optional.
`sort_dir`	No	asc or desc. Defaults to desc.
`countries`	No	ISO-3166-1 alpha-2 country codes to include. Optional.
`platforms`	No	AI platforms to include. Optional. Values: chatgpt, perplexity, gemini, grok, copilot, claude, google_ai_mode, google_ai_overview.
`project_id`	Yes	Project to compute analytics for.
`start_date`	Yes	Inclusive UTC start of the analytics window. ISO 8601 date (YYYY-MM-DD).

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 15, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive, closed-world behavior, and the description still adds real value beyond them: the row grain (one row per tracked brand in scope), zero-fill semantics for brands without mentions, null sentiment/position for those rows, and pagination. It does not cover ordering interactions (e.g., where zero-visibility brands land under sort_by) or result-set size, which keeps it from a 5.

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: what is returned, what the row grain and zero-fill behavior are, then pagination. The most decision-relevant content (metric list and grain) is front-loaded and nothing is redundant.

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 13 parameters and no output schema, the description carries the burden of explaining the return shape, and it does so well by defining the row-per-brand contract and the null/zero cases. Remaining gaps are minor: it does not say how interval is ignored for this aggregate tool, nor how sorting treats zero-visibility rows.

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 100%, so every parameter (project_id, start_date, end_date, filters such as segments, platforms, countries, tags, plus pagination and sorting) is already documented in the schema. The description only alludes to a 'filter window' and 'paginated' without adding format or interaction detail, 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?

The description names a specific verb+resource combination ('per-brand aggregates over the filter window') and enumerates the four metrics returned: visibility, share-of-voice, sentiment, and position. This clearly separates it from mention-level siblings like list-brand-mentions-tool, though it never explicitly contrasts itself with monitor- or platform-level analytics tools.

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?

Usage is implied rather than stated: an agent can infer this is the brand-grain aggregate view and that raw mentions belong elsewhere. There is no explicit when-to-use, no exclusion of similar analytics siblings (list-monitor-analytics-tool, list-platform-analytics-tool), and no statement of prerequisites beyond the schema's date constraints.

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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