Moonlings — Local Business Intelligence

Competitor Quick Scan

run_scan

Read-only

A live competitor scan: a research agent finds the business's strongest same-category rival nearby and scouts it (offer, pricing signals, review positioning, what they do that this business doesn't), while two ground-truth lookups run in parallel: ChatGPT/Perplexity sampling for AI-assistant visibility, and a direct Google Places review comparison (reviewSnapshot — the authoritative numbers; finding source URLs are verified against what the agent actually retrieved). The AI-visibility portion is a single-run snapshot — AI answers vary substantially between runs. BLOCKING and slow: typically 2-4 minutes — only call it from contexts that tolerate a long tool call. Price: $1.99 per delivered scan; a failed scan is never charged.

Input Schema

TableJSON Schema

Name	Required	Description
`address`	No	Optional: street address of the SPECIFIC location to scan, e.g. "332 Ludlow Ave" — use for chains/multi-location businesses so the right branch anchors the review comparison
`location`	Yes	City and state/region, e.g. "Cincinnati, OH"
`businessName`	Yes	The local business to scan
`businessType`	Yes	Category anchor, e.g. "ice cream shop" — rival discovery and AI sampling are both category-anchored

TDQS

A4.5/5.0

Behavior4/5

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

Annotations provide readOnlyHint=true, and description adds details about blocking behavior, run time, cost, and result delivery. 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?

Description is front-loaded with main purpose and includes essential details, but is somewhat lengthy. Could be slightly trimmed without losing informativeness.

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?

Despite lacking an output schema, description outlines what results include (offer, pricing, review comparison). Covers complexity and constraints adequately.

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 coverage is 100%, and description adds context beyond schema definitions, such as clarifying that address is for chains and businessType anchors rival discovery.

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?

Description clearly states it performs a live competitor scan, finding a rival and gathering specific data. It distinguishes itself from sibling tools like check_facts and get_report_result by covering a unique scanning function.

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

Usage Guidelines5/5

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

Explicitly states when to use ('only call it from contexts that tolerate a long tool call'), notes blocking nature and slow speed, and includes pricing and failure policy.

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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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.

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