AEO Copilot MCP Server

get_recommendations

Obtain prioritised, actionable recommendations to improve your brand's AI visibility. Insights are based on prompt results and a technical audit of your website.

Instructions

Get prioritised, actionable recommendations to improve your brand's AI visibility — based on prompt results and a technical audit of your website.

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_recommendations"
  }
}

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

B3.1/5.0

Behavior2/5

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

With no annotations, the description must disclose behavioral eatures itself. It states that recommendations are 'prioritised, actionable' and derived from prompt results and an audit, but it does not clarify whether the operation is read-only, whet it triggers any computation or analysis, or what side effects or dependencies may apply.

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?

The description is a single, well-prefaced sentence that front-loads the action and outcome while adding the evidential basis. There is no redundancy or filler.

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

Completeness3/5

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

For a one-paramter tool with no output schema, the description explains the purose and the data it relies on, but not the return shape or any prerequisites beyond an implicit audit and prompt results. It is adequate but not fully self-sufficient for an agent deciding whether results are ready.

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?

The input schema already fully documents brandId as 'The brand UUID from list_brands', so schema description coverage is 100%. The description adds context around 'your brand' but no additional parameter semantics; baseline 3 is appropriate.

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 resource ('recommendations') and a clear purpose: improving a brand's AI visibility. It also specifies the basis ('prompt results and a technical audit'), which differentiates it from sibling data-retrieval tools like get_insights and get_results, though it does not explicitly name an alternative.

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 that prompt results and a website audit should already exist, but it does not explicitly state when to choose this tool over siblings, nor does it mention any required prior steps. No exclusions or alternatives are provided, leaving usage inferable but under-specified.

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