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Scan business readiness

scan_business_readiness
Read-onlyIdempotent

Scan any business website and report whether AI agents can find, trust, and pay it. Returns pillar scores, a grade, per-check evidence, and a link to the report (it opens for whoever has the link and is not listed anywhere).

Input Schema

NameRequiredDescriptionDefault
urlYesThe business website, e.g. example.com

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive, so the safety profile is covered. The description adds genuine context beyond that: it discloses the return artifacts (pillar scores, grade, per-check evidence) and warns that the report link is unlisted and opens for anyone holding it, which is a meaningful behavioral detail.

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?

Two tight sentences with the outcome front-loaded and the output description following. The parenthetical about the unlisted link is slightly digressive but earns its place by warning about shareability.

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 no output schema, the description carries the burden of explaining return values and does so adequately (pillar scores, grade, per-check evidence, report link). Combined with rich annotations and full schema coverage, an agent has enough to call it correctly, though usage sequencing remains unaddressed.

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% and there is only one parameter (url) with an example format in the schema. The description adds no syntax, format, or constraint detail beyond what the schema already provides, so the baseline of 3 applies.

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 gives a concrete verb (scan) and resource (business website) and states the outcome precisely: whether AI agents can 'find, trust, and pay it.' It never names a sibling, so differentiation from tools like check_business_token or lookup_business is left to inference, which keeps it just short of a 5.

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

Usage Guidelines2/5

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

There is no when-to-use guidance, no prerequisites, and no mention of the eight sibling tools. A brief note such as 'use before place_order or after lookup_business' would be needed, since the purpose alone leaves the agent guessing about sequencing.

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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