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Official OpenAI Announcements & Model Releases — buy per-query in-session (openaiwatch)

check_earnings

Read-onlyIdempotent

Check how much I have earned and what is pending. Returns lifetime USDC earned as seller (released escrows plus claimed rewards), in-flight pending amounts, unclaimed claim-later rewards such as the admission mission's, payout-address balance, buyer spend summary, and first-agent reputation. Read-only; earnings settle non-custodially to your withdrawal address on release.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
reputationNo
pending_usdcNo
spend_summaryNo
payout_addressNo
unclaimed_usdcNo
how_to_get_paidYes
escrow_sales_usdcNo
wallet_balance_usdcNo
lifetime_earned_usdcNo
missions_earned_usdcNo
deferred_claimed_usdcNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

The annotations already declare readOnly, idempotent, and non-destructive. The description adds further behavioral context by explaining the read-only nature and the non-custodial settlement process, including details about released escrows and claimed rewards. This goes beyond the basic annotation hints.

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 description is composed of three sentences that pack a lot of information. It lists many return components but is not overly verbose. The structure is logical: first the check request, then the return details, then the behavioral note. It is concise enough for an agent to parse effectively.

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?

The description provides sufficient context for an agent to understand what the tool does and what to expect, including the read-only nature and the settlement behavior. However, it lacks explicit usage guidance or differentiation from sibling tools, which would improve completeness. Nevertheless, the core context is present.

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

Parameters4/5

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

The input schema is empty with no parameters, so there are no parameter semantics to describe. The description does not need to add anything about parameters; the baseline for 0 parameters is 4, and the description does not introduce any ambiguity.

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?

The description clearly states the tool's purpose and scope: checking earnings and pending amounts, enumerating specific components like USDC earned, in-flight pending, unclaimed rewards, payout balance, buyer spend, and reputation. The verb 'Check' is specific and the resource is 'earnings', which distinguishes it from other tools like find_paid_work or data_session_query.

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 does not explicitly state when to use this tool versus alternatives; it focuses on what it returns rather than providing selection criteria. However, the use case is fairly self-evident from the name and description, so this is a minor gap.

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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