AEO Copilot MCP Server

get_results

Retrieve AI prompt results showing a brand's mention, position, sentiment, sources, and competitors across ChatGPT, Claude, Perplexity, and Google AI Overviews. Filter by engine.

Instructions

Get prompt execution results for a brand across AI engines (ChatGPT, Claude, Perplexity, Google AI Overviews). Each result includes a per-engine block with the full answer text (response), plus whether your brand was mentioned, its position, sentiment, sources, and which competitors appeared. Set engine to return only one engine's answers — e.g. 'claude' to read exactly what Claude said.

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_results"
  }
}

Input Schema

Name	Required	Description
`to`	No	End date filter in ISO format (e.g. 2025-03-31)
`from`	No	Start date filter in ISO format (e.g. 2025-01-01)
`limit`	No	Max results to return (default 100, max 500)
`engine`	No	Return only this engine's answer/response for each prompt (e.g. 'claude'). Omit to get every engine's block.
`brandId`	Yes	The brand UUID from list_brands
`topicId`	No	Filter results to a specific topic UUID

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.0Aug 30, 2026

TDQS

A4.1/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses the response structure in detail (per-engine block, full answer text, brand mention, position, sentiment, sources, competitors) and explains the behavior of the engine parameter. Minor behavioral aspects like pagination or what happens with empty results are not mentioned, but the core behavior is well covered.

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 tightly written sentences. The core purpose is front-loaded, followed by the output fields and the engine filter behavior. There is no filler or repetition; every clause adds information an agent needs.

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

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite the absence of an output schema, the description gives sufficient detail about the return value (per-engine block with response and analysis fields). All six parameters are documented in the schema, and the description explains the default when engine is omitted. An agent can invoke the tool correctly with the information provided.

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%, and every parameter has a clear description. The tool description adds no new parameter-level meaning beyond giving 'claude' as a concrete example, which the schema already covers. Baseline 3 is appropriate because the schema does the heavy lifting.

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 opens with a specific verb and resource: 'Get prompt execution results for a brand across AI engines.' It goes on to enumerate exactly what each result contains (response text, brand mention, position, sentiment, sources, competitors), which clearly differentiates it from sibling tools like get_index_results by anchoring it to brand-level, not index-level, results.

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 context is implied by the phrase 'for a brand,' but the description never explicitly says when to choose this tool over alternatives such as get_index_results or get_insights. No when-not-to-use guidance or named sibling tools are provided.

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