Verify an AI Agent API’s x402, agent.json, OpenAPI, llms.txt, robots.txt, and MCP discovery surfaces; return a 0–100 score, missing fields, validation findings, and prioritized fixes. $1.00/call via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Bare domain (example.com) or https URL — normalized to the https origin |
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?
No annotations are provided, so the description carries the behavioral burden. It does disclose the $1.00/call monetization, which is a genuine economic trait. However, it does not cover failure modes, rate limits, or behavior on invalid/non-AI domains, and the normalization detail lives in the schema rather than here.
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?
One dense sentence that front-loads the action, explicitly enumerates the audited surfaces, and lists all return components. The cost is tacked on cleanly at the end. Minimal waste, though the missing usage guidance is an absence rather than a conciseness issue.
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, it reasonably covers the audit scope, output constituents (score, missing fields, findings, prioritized fixes), and cost. It would be more complete if it distinguished itself from the overlapping x402/b402 siblings, but it is adequate for invoking correctly.
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 single 'domain' parameter is already well described (bare domain or https URL, normalized to https origin). The description adds cost context but no parameter-specific semantics, so the baseline 3 for full schema coverage 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 names a specific verb (verify) and a specific resource (AI Agent API discovery surfaces: x402, agent.json, OpenAPI, llms.txt, robots.txt, MCP), and lists concrete outputs. It is clear about what it does, though it does not explicitly contrast with the narrower sibling tools (x402_endpoint_verify, b402_preflight), so differentiation is implicit rather than stated.
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?
No guidance on when to use this audit versus its siblings (x402_endpoint_verify, b402_preflight, fetch_x402_content). It does mention the $1.00/call cost, which hints at a decision factor, but there are no exclusions or alternative routing, leaving the agent to infer when the breadth of this audit is warranted.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.