Verify business legitimacy by domain or name in <2s. Trust score, verdict, evidence. x402-payable.
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Average 4.3/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of overlap or confusion. The tool's purpose is clearly defined and distinct.
The single tool name 'check_business' follows a consistent verb_noun pattern, making it predictable and descriptive.
The server has a narrow, single-purpose scope (business validation), and one well-designed tool is entirely appropriate. The count is well-scoped for the intended use case.
The single tool provides a comprehensive verdict with score, confidence, and underlying signals, fully covering the domain of business verification. No additional operations are necessary for the stated purpose.
check_businessAInspectVerify whether a domain belongs to a real business. Returns a verdict (real/likely_real/uncertain/likely_fake/fake), a 0-100 score, confidence, and underlying signals (WHOIS age, SSL, homepage LLM judgment, contact info, social presence). Costs 5 cents per fresh call; cached results are free for 24h.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | The domain to check, e.g. "stripe.com" (without https:// or path). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It discloses outputs (verdict, score, confidence, signals), lists specific underlying signals, and explains cost/caching behavior. It doesn't explicitly state that this is a read-only network operation, but that is implicit from 'verify' and 'fresh call.' It also doesn't mention error handling or rate limits, but the provided behavioral details are solid.
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 three sentences, front-loaded with the core purpose, followed by output details and cost implications. Every sentence adds value without any filler, making it highly concise and well-structured.
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 is remarkably complete. It explains the verdict scale, score range, confidence, and underlying signals, plus cost and caching. The only minor omission is error handling, but given the tool's simplicity, the description covers essential context sufficiently.
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?
Schema coverage is 100% and the schema already provides a clear description of the 'domain' parameter with an example and formatting constraint. The description adds no extra parameter-level semantics, so the baseline score of 3 is appropriate.
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 clearly states the tool's function: 'Verify whether a domain belongs to a real business.' This is a specific verb-resource pair, and the detailed output list (verdict, score, confidence, signals) reinforces the purpose. With no sibling tools, differentiation isn't needed, and the purpose is unambiguous.
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 implies when to use the tool (to assess domain legitimacy) but doesn't provide explicit when-not-to-use or alternatives. It does give practical usage context, such as 'Costs 5 cents per fresh call; cached results are free for 24h,' which helps the agent decide on repeated calls. This is clear context but lacks exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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