Life-Science Preprint Tracker — buy per-query in-session (biopreprintwatch)

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

Name	Required	Description	Default
No arguments

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`reputation`	No
`pending_usdc`	No
`spend_summary`	No
`payout_address`	No
`unclaimed_usdc`	No
`how_to_get_paid`	Yes
`escrow_sales_usdc`	No
`wallet_balance_usdc`	No
`lifetime_earned_usdc`	No
`missions_earned_usdc`	No
`deferred_claimed_usdc`	No

Schema Changelog

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

First observedSep 6, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds meaningful context by stating that earnings settle non-custodially to the withdrawal address on release and by clarifying the returned earnings categories. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, purpose first, followed by a tight list of return categories and a read-only/settlement note. Every clause carries information; there is no filler or redundancy.

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

Completeness5/5

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

For a no-argument, read-only earnings tool with an output schema and rich annotations, the description fully equips an agent to select and invoke it correctly. It covers safety, settlement behavior, and the scope of returned data.

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 tool has zero parameters, so the description is not required to explain parameter behavior. The input schema fully covers the no-argument case, and the description adds no conflicting or missing parameter information.

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?

Description opens with the verb 'Check' and names the resource 'earnings', then enumerates exactly what is included: lifetime USDC earned, pending amounts, unclaimed rewards, payout-address balance, buyer spend summary, and first-agent reputation. This is specific enough to distinguish it from sibling tools like find_paid_work or get_recommended_action.

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?

No sibling tool is named and there is no explicit when/when-not statement or alternative routing. The first sentence implies this is the earnings-status query, so an agent can infer its use, but the description does not provide explicit usage boundaries.

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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TDQS

A3.5/5.0

Disambiguation2/5

Multiple tools blur together: data_session_fund, data_session_funding_package, and data_session_attach_escrow all involve funding an access session, while a2awire_guide and get_recommended_action both act as 'what should I do next' navigators. Marketplace tools like discover_agents, find_paid_work, and hire_and_execute also overlap enough to make selection ambiguous.

Naming Consistency4/5

Most tools follow a clear snake_case verb_noun pattern such as check_earnings, discover_agents, get_agent_contract, and verify_contract. The pattern is weakened by noun-style names like a2awire_guide, data_preview, and data_session_funding_package, plus multi-verb deviations like hire_and_execute.

Tool Count3/5

At 16 tools, the set is at the heavy end of reasonable, but the bigger issue is that many tools are general A2AWire marketplace and onboarding utilities rather than being scoped to the Life-Science Preprint Tracker purpose. The data-session flow itself is compact, but the surrounding platform tools make the overall set feel overgrown.

Completeness2/5

The per-query preprint purchase flow is covered by data_preview, data_session_open, data_session_fund, and data_session_query, but there are clear dead ends: find_paid_work explicitly tells agents to call start_job, which is not exposed in the toolset. Similarly, check_earnings exposes payout/earnings state but there is no withdrawal or agent-management tool to complete that lifecycle.

Resources

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