ai-visibility-mcp

Check AI visibility

check_visibility

Read-only

Runs customer questions across configured AI engines and reports whether a business is named, where it ranks, evidence, and which competitors appear instead.

Instructions

Run customer questions across the configured AI engines and report, per engine and per question, whether the business was named, its position in the list, a short evidence quote and which competitors were named instead. Handles name variants (LLC/Inc, "&" vs "and", spacing, small typos). Pass aliases for nicknames or old names.

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": {
      "business": "<business>",
      "questions": "<questions>"
    },
    "name": "check_visibility"
  }
}

Input Schema

Name	Required	Description
`domain`	No	Website, used as an extra match signal
`aliases`	No
`engines`	No
`business`	Yes	Business name, for example "Eco-Cool HVAC"
`questions`	Yes

Output Schema

Name	Required	Description	Default
`stats`	Yes
`errors`	Yes
`engines`	Yes
`overall`	Yes
`results`	Yes
`business`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 3, 2026

TDQS

A3.5/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds real behavioral value by describing the per-engine/per-question result shape (named?, position, evidence quote, competitors) and the normalization behavior for name variants (LLC/Inc, "&" vs "and", spacing, typos). It says nothing about cost, latency, credit consumption, or what happens when engines is omitted, which for a multi-engine fan-out call is meaningful missing context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with the operation and the reported fields, then the normalization behavior, then the alias instruction. The parenthetical list of variant types earns its space because it sets matching expectations. Minor density, but no filler sentences.

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?

With an output schema present, the description does not need to enumerate return values, yet it still sketches them usefully. The main gaps are operational ones: whether multiple engines consume more quota or time, what engine set is used when engines is omitted, and any guidance on question phrasing or limits. For a read-only, open-world tool of this complexity, it is close to sufficient.

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 only 40%: business and domain are documented, while aliases, engines, and questions have no schema descriptions. The description compensates for aliases (nicknames, old names) and clarifies that matching tolerates variants, and 'configured AI engines' hints that engines may default, but questions (the core required input, max 50) and domain as a match signal get no added meaning.

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 uses a specific verb+resource framing: run customer questions across AI engines and report per engine and per question whether the business was named, its rank, an evidence quote, and which competitors appeared. That reporting shape implicitly separates it from ask_engines (raw answers) and compare_competitors, but no sibling is named explicitly, so sibling differentiation is left to inference.

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 domain of use (AI visibility auditing of a business across engines) is clear from the framing, but there is no explicit when-to-use guidance, no when-not-to-use, and no routing to ask_engines, suggest_questions, or compare_competitors. The only actionable guidance is the parameter-level note about passing aliases for nicknames or old names.

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

Deploy Server