Moonlings — Local Business Intelligence

AI Visibility Check

check_visibility

Read-only

Ask ChatGPT and Perplexity live (with web search) who they'd recommend in a business's category and city, and report whether THIS business appears. Returns honest appearance counts (never an invented metric), a per-engine split, who gets recommended instead, and which local sites the AI answers cite. AI answers vary substantially between runs — one check is a snapshot, not a stable measurement; re-check over time for the real picture. Call this when a user wants to know if AI assistants recommend a local business. Takes ~10-30 seconds. Price: $0.79 per delivered check.

Input Schema

TableJSON Schema

Name	Required	Description
`website`	No	Optional: the business website — a cited domain counts as an appearance
`location`	Yes	City and state/region, e.g. "Cincinnati, OH"
`businessName`	Yes	The local business to check
`businessType`	Yes	Category anchor, e.g. "gym" — the AI assistants are asked category questions, so this is required

TDQS

A4.3/5.0

Behavior4/5

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

The description adds significant context beyond the readOnlyHint annotation: it explains the live query mechanism, returns honest counts never invented, details the output components, notes variability between runs, and provides timing and pricing. No contradiction with annotations.

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?

The description is a single paragraph but well-structured: starts with purpose, then details output and caveats, and ends with usage trigger. It is informative without being overly verbose, though some sentences could be tightened.

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?

Without an output schema, the description compensates by detailing what is returned (appearance counts, per-engine split, alternatives cited, local sites). It also covers timing, pricing, and caveats. This makes the tool's behavior understandable for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% with clear descriptions for all 4 parameters. The tool description adds value by explaining that website is optional and a cited domain counts as appearance, and why businessType is required for the AI queries.

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's purpose: to check whether a local business appears in AI assistant recommendations (ChatGPT, Perplexity) for a given category and location. It distinguishes itself from sibling tools by focusing on AI visibility rather than fact-checking or report generation.

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 explicitly says 'Call this when a user wants to know if AI assistants recommend a local business' and warns about variability, advising re-checking over time. It does not explicitly state when not to use, but the guidance is sufficient.

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

All Moonlings — Local Business Intelligence tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.4/5.0

Disambiguation5/5

Each tool serves a distinct function: fact verification, visibility check, review gap analysis, competitor scan, and deep research. The report-related tools (start, poll, fetch) are clearly separated by their roles. No two tools are easily confused.

Naming Consistency4/5

All names use snake_case consistently. Most start with verbs like 'check_', 'get_', 'run_', or 'start_', though 'ping' and 'review_gap' deviate slightly. The pattern is predictable and readable.

Tool Count5/5

Eight tools cover the domain of local business intelligence well: fact-checking, visibility, review comparison, competitor scan, and deep research. The count is neither too sparse nor overwhelming for the scope.

Completeness4/5

The tool surface covers core intelligence needs: fact verification, review comparisons, visibility checks, competitor scans, and deep research reports. Minor gaps like a tool to list businesses or manage multiple entities exist, but for single-business analysis it's nearly complete.

Resources

Need Help?