Pineprompt

List Platform Analytics Tool

list-platform-analytics-tool

Read-onlyIdempotent

Per-AI-platform aggregates (visibility, share-of-voice, sentiment, position, used_count) scoped to a single brand. One row per platform present in the filtered response set; sorted by visibility desc.

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>",
      "brand_id": "<brand_id>",
      "start_date": "<start_date>",
      "end_date": "<end_date>"
    },
    "name": "list-platform-analytics-tool"
  }
}

Input Schema

Name	Required	Description
`tags`	No	Tag IDs (prompt classifiers) to limit responses by. Optional.
`brand_id`	Yes	Numeric ID of the brand to scope per-platform metrics to. Must belong to the project.
`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.
`segments`	No	Segment IDs (brand groupings) to slice analytics by. Optional.
`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 declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds genuinely new behavioral facts: one row per platform actually present in the filtered response set (i.e. absent platforms are omitted, not zero-filled) and a deterministic sort by visibility descending. It does not discuss pagination or null/empty-result behavior, but for a read-only aggregate that is a minor gap.

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?

Two dense sentences with zero filler: metrics, scope, row cardinality and ordering are all front-loaded. Each clause carries information an agent needs to interpret the result.

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 no output schema, the description correctly compensates by naming the returned measures, the row grain and the sort order. With 10 parameters at full schema coverage, little else is required. Only the behavior of empty/absent platforms and any filter interaction is left implicit.

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 the schema already explains project_id, brand_id, date bounds, interval semantics and the platform enum values. The description adds only the notion that filters define a 'filtered response set' and reinforces single-brand scoping; it does not clarify interactions among the optional filters (tags, monitors, segments, countries, platforms).

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 resource (per-AI-platform aggregates), enumerates the exact metrics returned (visibility, share-of-voice, sentiment, position, used_count), and pins the scope to a single brand. The grouping dimension 'per AI platform' is what separates it from the brand/monitor/prompt analytics siblings, though no sibling is named explicitly.

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 only implied: the word 'aggregates' signals this is the aggregate variant (so the schema's note that interval is 'ignored for aggregate tools' applies), and 'scoped to a single brand' implies brand_id is mandatory. There is no explicit when-to-use versus when-not, and no routing guidance toward the sibling analytics tools that group by brand, monitor or prompt.

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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