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agentscan: identify an x402 client by User-Agent: monitor, indexer, grader or HTTP library

identify_x402_client
Read-onlyIdempotent

agentscan: Identify an x402 client by User-Agent: monitor, indexer, grader or HTTP library, with call rate. For any other User-Agent use parse_user_agent; for a whole access log use analyze_agent_traffic. Returns whether a User-Agent is a known x402 monitor, indexer, grader, SDK, AI fetcher, bot or HTTP library, what it does and its observed call rate at one seller (requests a day, per route, unpaid share); no ua: the full table. Input: optional ua; GET takes it as a query parameter. Reviewed table from one seller's logs and public docs; observed kinds are not confirmed by the operators. Unknown is known false (charged). The ua is not stored. Typically under 0.1 s. Price: USD 0.001. Free: 10 agent-index calls per IP per UTC day. Docs: https://tanod.dev/learn/x402-client-identify-api.html

Input Schema

NameRequiredDescriptionDefault
uaNoOptional: a User-Agent string to identify (at most 512 characters; not stored or echoed). Empty or absent: the whole table.

Output Schema

NameRequiredDescriptionDefault
modeNo
noteNo
countNo
errorNoOnly on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object.
kindsNo
knownNo
clientNo
methodNo
clientsNo
versionNo
kind_meansNo
matched_byNo
family_labelNo
product_tokenNo
observed_windowNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, but the description adds substantial context beyond them: pricing (USD 0.001), a free tier, latency (~0.1 s), privacy ('The ua is not stored'), provenance ('observed kinds are not confirmed by the operators'), and billing behavior ('Unknown is `known` false (charged)'). This is exactly the behavioral disclosure structured fields cannot carry.

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

Conciseness4/5

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

Purpose is front-loaded and each clause carries information (alternatives, return shape, billing, privacy). It is dense and semicolon-heavy, reading as a single run-on paragraph, but there is little that is genuinely wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists so return values need not be explained, yet the description still summarizes the key fields, and annotations cover the safety profile. Pricing, privacy, provenance, latency, and sibling routing are all present, leaving an agent nothing essential missing before calling.

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 coverage is 100% and the schema already documents the `ua` semantics including the empty/absent case ('Empty or absent: the whole table'), which the description repeats. The only added detail is the HTTP transport note ('GET takes it as a query parameter'), which is marginal. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (identify) and resource (an x402 client by User-Agent), and enumerates the categories it recognizes (monitor, indexer, grader, HTTP library). It also names sibling alternatives (parse_user_agent, analyze_agent_traffic), so an agent can distinguish it from related tools without opening any schema.

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

Usage Guidelines5/5

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

Explicit when-to-use routing: 'For any other User-Agent use parse_user_agent; for a whole access log use analyze_agent_traffic.' This gives both the alternative tools and the conditions that select them, leaving nothing to inference.

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

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