Moonlings — Local Business Intelligence

AI Fact-Check

check_facts

Read-only

Find what AI assistants get WRONG about a local business. Asks ChatGPT and Perplexity live (with web search) about the business's hours, address, phone, and category, then verifies each stated fact against Google Business ground truth. Returns a severity-ranked list of conflicts (with the AI's value vs. the trusted value and source) plus discrepancies to check. Conservative by design: a claim with no trusted source is 'unverifiable' (never an error), and a conflict is only counted when it reproduces across engines — so it won't cry wolf. Call this when a user asks whether AI has the right info about a business, or 'why does ChatGPT say we're closed'. Takes ~15-30 seconds. Price: $1.49 per delivered check.

Input Schema

TableJSON Schema

Name	Required	Description
`website`	No	Optional: the business website — if an AI answer cites this domain, a mismatch is treated as a possibly-stale discrepancy rather than an error
`location`	Yes	City and state/region, e.g. "Cincinnati, OH"
`businessName`	Yes	The local business to fact-check
`businessType`	No	Optional category anchor, e.g. "restaurant" — helps resolve the business; not required

TDQS

A4.6/5.0

Behavior5/5

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

The description thoroughly discloses behavioral traits: live queries to two AI engines, verification against Google Business, pricing ($1.49), duration (15-30 seconds), and conservative conflict criteria (only counts conflicts that reproduce across engines). This adds value beyond the readOnlyHint annotation, which is consistent.

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 well-structured and front-loaded with the purpose. It is somewhat lengthy but every sentence adds value (process, conditions, pricing). Could be slightly more concise, but efficient overall.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with no output schema, the description fully explains the return (severity-ranked list of conflicts and discrepancies). It covers all necessary context: process, pricing, timing, and behavior. No gaps remain.

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?

With 100% schema coverage, the description adds meaning by explaining how parameters are used: the 'website' parameter triggers special discrepancy handling, and 'businessType' aids in resolving the business. These details go beyond the schema descriptions.

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: fact-checking AI claims about a local business's hours, address, phone, and category by querying ChatGPT and Perplexity and verifying against Google Business ground truth. It distinguishes itself from siblings by specifying the unique function of AI fact-checking.

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 when to call this tool: 'when a user asks whether AI has the right info about a business' or for 'why does ChatGPT say we're closed.' It also explains the conservative behavior (won't cry wolf). While it doesn't mention when not to use it or alternatives, the context is clear and sufficient.

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.

Resources

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