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APEX Faucet

by xyz.apexfaucet
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Web answer

web_answer
Read-onlyIdempotent

Answer a question from the live web with quotes that are whole sentences our code took word for word from the pages it fetched; no quote, no answer. The answer is AI-written; the quotes are exact. $0.01 per call over x402.

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": {
      "q": "<q>"
    },
    "name": "web_answer"
  }
}

Input Schema

NameRequiredDescriptionDefault
qYesthe question, 10 to 300 characters
paymentNoa signed x402 payment (the same base64 payload you would put in the PAYMENT-SIGNATURE header). Pass it here and the purchase completes inside this tool call.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.7/5.0
Behavior4/5

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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses cost ($0.01 per call), the x402 payment mechanism, and the crucial behavioral fact that the prose answer is AI-written while quotes are exact source text, with refusal when no quote exists. Only latency, quote count, and fetch breadth are left unstated.

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?

Two compact sentences: the output guarantee is front-loaded, followed by the cost/payment note. The semicolon-separated clauses and 'no quote, no answer' carry real information, though the pricing sentence is somewhat compressed and reads as an addendum.

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 carries the burden of describing the return value, and it does: an AI-written answer plus verbatim sentence quotes, with no answer when no quote can be sourced. Payment handling is also covered; what remains unstated (how many quotes, what a failed fetch returns) is minor for a single-question tool.

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%, so both parameters are already fully documented, including the 10–300 character bound on q and the PAYMENT-SIGNATURE equivalence for payment. The description reinforces that passing payment completes the purchase in-call and frames q as a question, but adds little syntax or format detail beyond the schema — baseline 3.

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?

Names a specific verb+resource (answer a question from the live web) and adds a distinctive output guarantee: sentence-exact verbatim quotes with 'no quote, no answer'. It is clear what the tool produces, though it never explicitly contrasts itself with the sibling web_read, leaving the agent to infer the difference.

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?

Usage context is implied by 'answer a question from the live web' plus the per-call price, so an agent can infer this is for fresh, question-shaped lookups rather than page fetching. There is no explicit when-not-to-use statement or named alternative (e.g., web_read for raw page content), so routing depends on 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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