ai-visibility-mcp

Compare competitors

compare_competitors

Read-only

Run customer questions across AI engines to rank which businesses are named, how often, by which assistants and at what average position; flag your business in the ranking.

Instructions

Run customer questions across the configured AI engines and rank which businesses get named most, with how often, by which assistants and at what average position. Pass the business name to flag it in the ranking.

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": {
      "questions": "<questions>"
    },
    "name": "compare_competitors"
  }
}

Input Schema

Name	Required	Description
`top`	No	How many businesses to return (default 15)
`aliases`	No
`engines`	No
`business`	No
`questions`	Yes

Output Schema

Name	Required	Description	Default
`stats`	Yes
`answered`	Yes
`competitors`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 3, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations only declare readOnlyHint and openWorldHint, and the description adds real operational context: queries fan out across configured engines and results are aggregated into a ranked table with frequency, assistant attribution, and average position. It stops short of disclosing cost, latency, or per-engine call volume for question-times-engine fan-out, which would matter for a batch operation against external models.

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 sentences with zero filler, front-loading the operation and what the ranking contains. The second sentence cleanly handles the one non-obvious parameter behavior without repeating schema details.

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?

An output schema exists, so the return structure does not need spelling out, and safety is covered by annotations. The remaining gaps are material for a 5-parameter batch tool: 'aliases' is entirely unmentioned, engine selection semantics are ambiguous ('configured' versus the engines array parameter), and there is no guidance on question volume or sibling selection.

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 only 20% (just 'top' is documented), so the description must carry weight: it clarifies 'questions' as customer questions, 'business' as a flag rather than a filter, and implies what 'engines' controls via 'configured AI engines.' It is silent on 'aliases' entirely and gives no indication that 'business' does not restrict results, which leaves two of five parameters undocumented anywhere.

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?

Uses a specific verb-plus-resource framing: run customer questions across AI engines and rank businesses by mention frequency, assistant, and average position. That makes the operation's output shape immediately legible. It does not explicitly contrast itself with ask_engines or check_visibility, so an agent must infer the boundary from the sibling names alone.

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?

The comparative ranking framing implies when this tool fits (multi-business competitor comparison), and 'Pass the business name to flag it in the ranking' gives a light usage hint. However, no alternative is named, no exclusion conditions are stated, and nothing tells the agent when to prefer check_visibility or ask_engines instead.

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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