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Ninar AI

by ai.ninar
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Run an AI visibility scan

scan_visibility

Run an AI visibility scan for a brand. Pass city for a local-business check (ChatGPT + Gemini, city-scoped). Omit city for a multi-engine GEO scan across ChatGPT, Gemini, Perplexity, Claude, AI Overviews — engine count scales with the user's Ninar plan (free = 2).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNoCity for a local-business check. Omit for multi-engine GEO scan.
countryNoOptional ISO country: us, gb, in, eu.
websiteNoOptional brand URL for GEO citation matching.
categoryYesCategory, e.g. 'AI visibility platform', 'pizza restaurant'.
use_caseNoOptional GEO use case, e.g. 'for sales teams'.
brand_nameYesBrand to scan, e.g. 'Ninar', 'Joe's Pizza'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed6 schema fields changed
    • changedInput schema / properties / brand_name / description
      Previous value: -"The brand to scan, e.g. 'Ninar', 'Stripe', 'Notion'."New value: +"Brand to scan, e.g. 'Ninar', 'Joe's Pizza'."
    • changedInput schema / properties / category / description
      Previous value: -"Category the brand competes in, e.g. 'AI visibility platform', 'CRM', 'note-taking app'."New value: +"Category, e.g. 'AI visibility platform', 'pizza restaurant'."
    • addedInput schema / properties / city
      Added value: +{
+  "description": "City for a local-business check. Omit for multi-engine GEO scan.",
+  "type": "string"
+}
    • addedInput schema / properties / country
      Added value: +{
+  "description": "Optional ISO country: us, gb, in, eu.",
+  "type": "string"
+}
    • changedInput schema / properties / use_case / description
      Previous value: -"Optional: specific use case to anchor the discovery prompts, e.g. 'for sales teams'."New value: +"Optional GEO use case, e.g. 'for sales teams'."
    • changedInput schema / properties / website / description
      Previous value: -"Optional: brand website URL — used for citation matching."New value: +"Optional brand URL for GEO citation matching."
  2. Changed5 schema fields changed
    • addedInput schema / properties / brand_name / description
      Added value: +"The brand to scan, e.g. 'Ninar', 'Stripe', 'Notion'."
    • addedInput schema / properties / category / description
      Added value: +"Category the brand competes in, e.g. 'AI visibility platform', 'CRM', 'note-taking app'."
    • removedInput schema / properties / city
      Removed value: -{
-  "description": "City to scope the scan to. Defaults to 'New York'.",
-  "type": "string"
-}
    • addedInput schema / properties / use_case
      Added value: +{
+  "description": "Optional: specific use case to anchor the discovery prompts, e.g. 'for sales teams'.",
+  "type": "string"
+}
    • addedInput schema / properties / website
      Added value: +{
+  "description": "Optional: brand website URL — used for citation matching.",
+  "type": "string"
+}
  3. First observed

TDQS

A4.2/5.0
Behavior4/5

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

The description adds behavioral context beyond annotations by revealing that engine count scales with the user's plan (free = 2) and listing the engines scanned. It doesn't mention side effects or rate limits, but annotations already indicate non-destructive behavior, so no contradiction.

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 concise sentences, front-loaded with the main action, with no filler or repetition. Every sentence adds useful information about the two modes and plan-scaling behavior.

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?

The description adequately explains the core behavior, including the decisive city parameter and plan-dependent scaling. However, it does not describe the output format or explicitly differentiate from the sibling audit_brand_visibility, leaving some gaps for a tool with six parameters and no output schema.

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%, so baseline is 3. The description's mention of city passing/omitting partially reflects the schema's own description of the `city` parameter, adding little new parameter-level meaning beyond what the schema already provides.

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 clearly states the tool runs an AI visibility scan for a brand, with specific modes (local-business vs multi-engine GEO) and names the engines involved. This specific verb+resource+scope is sufficient to distinguish it from sibling tools like audit_brand_visibility.

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 gives explicit instructions on when to pass `city` vs omit it, defining two distinct use cases. However, it does not mention alternatives or when not to use this tool compared to siblings such as audit_brand_visibility, so it lacks full exclusion guidance.

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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