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ExchangeRebel — pay-per-call APIs for AI agents (x402 on Solana)

macro_payrolls

US payrolls series (BLS, public domain). $0.005 per call — USDC on Solana via x402 (over MCP: call without payment to get the PaymentRequired challenge, then retry with the payment at _meta["x402/payment"]). HTTP equivalent: GET https://exchangerebel.com/toll/macro/payrolls.

Input Schema

NameRequiredDescriptionDefault
endNoEnd period
startNoStart period

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior4/5

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

Discloses behavior the annotations cannot: exact price ($0.005/call), payment rail (USDC on Solana), and that the first unpaid call returns a PaymentRequired challenge rather than data. Annotations say readOnlyHint=false and openWorldHint=true, consistent with a paid external call. Missing: whether the probe call itself is billed, and any rate limit or caching behavior.

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-loads the data resource, then the commercial/transport details in one tight sentence plus an HTTP equivalent. No filler, though the inline JSON key makes the middle clause dense to parse.

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

Completeness3/5

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

No output schema exists, so the description carries the burden of describing the return, and it never says what a payrolls series looks like (units, thousands of jobs, seasonally adjusted or not, frequency). The payment mechanics are thorough, but the actual shape of the returned data is left unspecified.

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 baseline 3 applies, but the schema text is thin ('Start period' / 'End period') and the description adds nothing about accepted date format, granularity (monthly?), or default range when both are omitted. Without that, an agent must guess at formats for a time-series query.

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 the specific resource (US payrolls series, BLS, public domain) and pairs it with a concrete HTTP endpoint, so an agent knows exactly what data it gets. It doesn't explicitly differentiate itself from macro_unemployment or macro_cpi in the sibling list, but the resource name is distinctive enough to route correctly.

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 payment workflow is spelled out precisely (challenge-then-retry with _meta["x402/payment"]), which is real invocation guidance. But there is no guidance on when to pick this series versus the other macro_* siblings, and no statement of what happens if start/end are omitted (required parameters = 0).

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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