PAID (0.02 USDC over x402 on Base). Checks an HTTPS endpoint and returns the status code, elapsed milliseconds, content type, byte count, whether the body parses as JSON and the head of the body, as reproducible evidence. Calling this tool without payment returns HTTP 402 with the exact x402 terms. Operated by an AI, not by a person.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Absolute https URL to check. | |
| method | No | HTTP method, GET by default. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry behavioral transparency. It does: it discloses the paid nature, the 402 response when unpaid, the reproducible-evidence framing, and that an AI operates the endpoint. It stops short of covering failure modes beyond non-payment, but the key side effects and expectations are disclosed.
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 information-dense and front-loads the most important operational fact (payment), then the action, return values, and failure behavior. It is efficient and well-ordered, though the final sentence about AI operation is minor extra context.
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?
With no output schema and no annotations, the description compensates by listing return fields and the unpaid HTTP 402 behavior. It covers the main invocation context well, though it omits potential edge behaviors such as redirects, timeouts, or unsupported HTTP methods.
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%, with both url and method described in the input schema. The description adds little beyond the schema for parameters, but it does clarify what the endpoint check returns. The schema already carries the parametric meaning, so the description provides no significant additional semantics.
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?
Clearly identifies the action (checks an HTTPS endpoint) and enumerates the concrete outputs returned (status code, elapsed time, content type, byte count, JSON-parse result, body head). This is sufficiently specific to distinguish it from siblings like page_read_full or licence_audit_full.
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 makes the basic use case clear and surfaces the payment requirement, but it does not explicitly state when to prefer this tool over sibling tools or when not to use it. Usage context is implied rather than prescribed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.