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Ai Model Experiments

Experiment Topup

experiment_topup
Read-onlyIdempotent

How to add prepaid credits for experiments (and your current balance). Payment is x402 — USDC on Base, paid in-band by any wallet-equipped agent: POST https://gateway.pipeworx.io/credits/topup?amount_usd=10 responds HTTP 402 with payment requirements; retry with PAYMENT-SIGNATURE to settle and the credits land instantly. Example: experiment_topup({})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
amount_usdNoIntended top-up amount in USD (1-500) — echoed into the instructions

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld, non-destructive, but the description adds real value beyond them: the payment rail (x402/USDC on Base), the in-band 402-then-PAYMENT-SIGNATURE flow, and that settlement credits land instantly. It does not cover auth requirements, rate limits, or failure modes, but for this unusual mechanism the disclosure is well above baseline.

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?

Front-loaded with the purpose, then the mechanism, then a concrete call example, with no filler sentences. It is dense and slightly technical but every clause earns its place. The trailing example experiment_topup({}) usefully shows a zero-arg invocation.

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?

There is no output schema, so the description must convey returns, and it does: the how-to instructions plus the caller's current balance. For a payment-flow tool of this complexity, the description is largely self-sufficient, though it omits error/retry edge cases.

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% for the single amount_usd parameter, so the schema already carries the semantics (range 1-500, echoed into instructions). The description only shows it embedded in an example URL (?amount_usd=10), adding marginal meaning. Baseline 3 is appropriate.

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?

States a specific resource (prepaid experiment credits) and its two outputs: how-to instructions and current balance. It is clearly distinguishable from siblings like experiment_create, experiment_estimate, or subscribe. The framing is slightly indirect ("How to add..." reads as documentation), but an agent can tell what the tool returns.

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?

The description thoroughly explains HOW to complete the payment (POST the gateway URL, handle the HTTP 402, retry with PAYMENT-SIGNATURE), which is strong operational guidance. However, it never says WHEN to choose this tool over alternatives (e.g., subscribe, or another credit mechanism) or any preconditions/exclusions, so selection guidance is only implied.

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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