scan_business_readinessScan any business website and report whether AI agents can find, trust, and pay it. Returns pillar scores, a grade, per-check evidence, and a public report URL.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The business website, e.g. example.com |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explains what the tool returns (pillar scores, grade, evidence, report URL) and implies a non-destructive scan, but it does not explicitly mention read-only behavior, potential latency, or whether the URL must be publicly accessible. This is adequate but leaves some behavioral details unstated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no fluff. The core purpose is stated first, followed by a concise list of return values. Every word earns its place and the structure is easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema, the description provides the essential context: what it does, what input it expects, and what it returns. It could include caveats about access or sync/async behavior, but the provided detail is largely sufficient for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers 100% of the single parameter with a clear description ('The business website, e.g. example.com'). The tool description adds minimal extra parameter meaning beyond reinforcing that the site should be a business website. Per the baseline rule for high schema coverage, this is sufficient.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Scan any business website') and a concrete outcome ('report whether AI agents can find, trust, and pay it'). It clearly defines what the tool does without ambiguity, and since there are no sibling tools, no differentiation is needed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description conveys a clear use case—evaluating a business's AI agent readiness—and implies it applies to any business website. It does not explicitly state when not to use it or list alternatives, but with no sibling tools, this is less critical. The context is sufficiently clear for an agent to decide when to invoke it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
With only one tool, there is no possibility of confusing it with others. The tool's purpose is clearly defined and unambiguous.
The single tool name 'scan_business_readiness' follows a clear verb_noun pattern, which is internally consistent and descriptive.
A single tool feels thin for a server, though it does encapsulate a complete scan operation. The count is borderline but not unreasonable for a focused single-purpose API.
The tool covers the full scan lifecycle from input URL to detailed report generation, including evidence and a public link. The only notable gap is the inability to fetch past reports without rescanning.