AEO Copilot MCP Server

get_insights

Analyze a brand's AI visibility with metrics on sentiment, competitive share, trends, and top topics to identify strengths and gaps.

Instructions

Get analytics insights for a brand: overall visibility score, sentiment breakdown, competitive share, visibility trends over time, top-performing topics, and competitor breakdown.

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": {
      "brandId": "<brandId>"
    },
    "name": "get_insights"
  }
}

Input Schema

Name	Required	Description	Default
`brandId`	Yes	The brand UUID from list_brands

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.0Aug 30, 2026

TDQS

A3.6/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full behavioral transparency burden. It clearly enumerates what kind of data is returned, which helps set expectations, but it does not disclose failure behavior, data freshness, required prerequisites, or any limitations. It is adequate but not richly transparent.

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?

A single, front-loaded sentence states the tool's purpose and then lists the concrete output components. There is no filler or repetition, and every clause contributes actionable information.

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 one-parameter read-style tool with no output schema, the description provides a solid list of return categories, which is enough for an agent to understand what it will receive. It could be more complete with notes on error cases or data scope, but the essentials are present.

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%, and the sole parameter brandId already has a descriptive schema comment ('The brand UUID from list_brands'). The description adds little beyond tying the tool to 'a brand', so the schema does the heavy lifting and a baseline of 3 is appropriate.

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?

The description uses a specific verb ('Get') and clearly identifies the resource ('analytics insights for a brand') while enumerating the exact components returned. This distinguishes it from sibling tools like get_results or get_index_share_of_voice, which focus on different resource types or scopes.

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

Usage Guidelines2/5

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

The description implies a use case—fetching brand-level analytics—but provides no explicit guidance on when to choose this tool over alternatives such as get_index_results, get_recommendations, or get_insights-related siblings. It lacks any when-not-to-use instructions or comparisons.

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