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Check payment requirements

check_payment_requirements
Read-only

Inspect an x402-protected URL without paying to retrieve its 402 payment options, identify which option would be paid, and verify whether it fits configured spending limits.

Instructions

Calls a URL without paying and returns its 402 payment options, which one would be paid, and whether it fits the limits.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>"
    },
    "name": "check_payment_requirements"
  }
}

Input Schema

NameRequiredDescriptionDefault
urlYesFull URL of the x402-protected endpoint
bodyNoRequest body. Objects/arrays are sent as JSON.
methodNoHTTP method (default GET)
headersNoExtra request headers

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

The annotations (readOnlyHint=true, openWorldHint=true) already cover the safety profile, and the description is consistent with them while adding real value: it discloses that the call is unpaid, that a 402 is expected, and that the response reports payment options, the selected option, and limit fit. It stops short of covering non-402 responses or error cases, but for a read-only probe this is solid disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence with zero filler, front-loading the non-obvious behavior (the call is made without paying) and then the return content. Nothing could be removed without losing information.

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 usefully summarizes what comes back, and all four parameters are covered by the schema. The only soft spot is "whether it fits the limits" — the source of those limits (wallet policy? configured cap?) is left implicit, which an agent may need to resolve.

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%, so url, body, method, and headers are all already documented in the schema, including the GET default and the JSON serialization note. The description adds no parameter-level meaning beyond that, which is the expected baseline when the schema does the work.

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 specific verb phrase ("calls a URL without paying") plus the concrete outcome ("returns its 402 payment options"), which is far more informative than the title alone. The "without paying" qualifier implicitly separates it from make_x402_request, though it never names that sibling explicitly.

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

Usage Guidelines3/5

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

"Without paying" implies this is a preflight/dry-run check to run before an actual paid request, but the description never states that as an instruction or names make_x402_request as the follow-up. Usage is inferable rather than specified; there is no explicit when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.