AEO Copilot MCP Server

run_brand_prompts

Run all brand prompts across ChatGPT, Claude, Perplexity, and Google AI Overviews, with optional topic filtering, and get the count of prompts executed.

Instructions

Run all prompts for a brand across every enabled LLM (ChatGPT, Claude, Perplexity, Google AI Overviews). Optionally filter to a single topic. Returns the count of prompts run.

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": "run_brand_prompts"
  }
}

Input Schema

Name	Required	Description	Default
`brandId`	Yes	The brand UUID from list_brands
`topicId`	No	Optional topic UUID — if provided, only that topic's prompts run

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.0Aug 30, 2026

TDQS

A3.7/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the action ('run prompts') and return value ('count'), but omits significant traits: invoking multiple external LLMs implies cost, latency, and non-idempotency, and there is no warning about side effects on existing results or whether the operation is synchronous. For a side-effecting execution tool, this is a meaningful transparency 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?

The description is two sentences with no fluff. The core action and scope are front-loaded, followed by the optional filter and return value. Every sentence earns its place.

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?

The description is adequate for a tool with two well-documented parameters and no output schema, covering the operation and its return value. However, it lacks operational caveats such as prerequisites (enabled LLMs), expected duration, how results are stored, and whether the operation can be safely repeated. For an execution tool with no annotation safety net, this is a noticeable completeness gap.

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 both parameters are already well-documented (brandId as the brand UUID from list_brands, topicId as an optional topic UUID). The description adds no new parameter-level meaning, so the baseline score of 3 applies.

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 states a specific verb ('Run'), a specific resource ('all prompts for a brand'), and the full scope ('across every enabled LLM'). It also distinguishes this from index-level or scanning tools by naming the brand-level operation and the optional topic filter. The purpose is unmistakable.

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

Usage Guidelines4/5

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

The description clearly indicates the brand-level use case and the optional topic filter, but it does not explicitly exclude or compare to sibling tools like run_index_prompts or scan_brand. The usage context is clear, but the when-not-to-use guidance is left to the agent's inference.

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