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

courts_disclosure_record

A judge's financial-disclosure records by judge id (header + investments, debts, gifts, agreements, positions, reimbursements, spousal and non-investment income). An empty list is an honest delivered answer. $0.01 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: POST https://exchangerebel.com/toll/courts/disclosure-record.

Input Schema

NameRequiredDescriptionDefault
judge_idYes
generationNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3.2/5.0
Behavior4/5

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

Annotations mark this as not read-only and not idempotent, and the description usefully explains why: each call is a metered $0.01 x402 payment requiring a challenge/retry handshake at _meta["x402/payment"]. It also discloses that an empty list is a legitimate delivered answer rather than a failure, and gives the HTTP endpoint. It stops short of saying whether repeated calls re-charge or how errors after payment are handled.

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?

The payload contents are front-loaded and the payment mechanics are placed after, so the reader learns what they get before how they pay. The single dense payment sentence is long but each clause (price, asset, challenge-retry, meta key) carries operational weight.

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?

For a paid, non-idempotent lookup with no output schema, the description covers cost, payment flow, and the shape of results at a section level. It omits the purpose and default of the 'generation' enum parameter and gives no sense of record size or response envelope, which are the remaining gaps an agent would hit.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% with two parameters. The description clarifies judge_id as the lookup key, but the 'generation' parameter (enum 2026-06-30 / 2026-09-30) is never mentioned in either the schema or the description, leaving an agent to guess what a disclosure 'generation' means and whether it is optional.

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 names a specific resource (a judge's financial-disclosure records), the lookup key (judge id), and enumerates the record sections returned (header, investments, debts, gifts, positions, etc.). That detail distinguishes its payload from the sibling courts_judge_record, though the relationship between the two is never stated explicitly.

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

Usage Guidelines2/5

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

There is no statement of when to use this tool rather than courts_judge_record, courts_court_record, or courts_changes, nor any prerequisites such as needing a judge id from a prior lookup. The only usage-adjacent guidance is how to interpret an empty result and how to complete payment, which does not help with tool selection.

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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