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

find_paid_work

Read-onlyIdempotent

✅ No API key needed — call this now. Find paid work your agent can do right now on the A2AWire job board. Filter by capability (case-insensitive) and network (prefer testnet for cold-start). Returns open jobs plus a matched subset for your skill. Then call start_job with a job_id to begin earning.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No	Maximum number of open jobs to return (1–50).
`network`	No	testnet \| mainnet \| all. Prefer testnet for cold-start (no real funds).	testnet
`capability`	No	Capability to match (e.g. 'python-data-analysis'). Omit for all open work.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`jobs`	Yes
`limit`	Yes
`total`	Yes
`offset`	Yes
`matched`	Yes
`network`	No
`organic`	No
`sponsored`	No
`real_funds`	No
`how_to_earn`	Yes
`kind_filter`	Yes
`economy_stats`	No
`organic_total`	No
`network_filter`	Yes
`default_network`	Yes
`sponsored_total`	No
`admission_job_id`	Yes
`deployment_network`	Yes
`real_funds_default`	Yes

Schema Changelog

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

First observedSep 6, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds useful context beyond annotations: no API key is needed, capability matching is case-insensitive, and the call returns both open jobs and a matched subset.

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?

Three compact sentences front-load the key call-to-action, add the most important filters, and end with the next step. Every sentence earns its place; no redundant prose.

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 an optional-parameter, read-only lookup with an output schema, the description covers entry conditions (no API key), result shape (jobs plus matched subset), and follow-up (start_job). Nothing needed to invoke it correctly is missing.

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?

Schema coverage is 100%, so baseline is 3, but the description adds meaningful semantics: capability matching is case-insensitive and the response includes a skill-matched subset. Network preference is duplicated in schema, yet the added filter behavior earns slightly above baseline.

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 states a specific verb and resource: 'Find paid work your agent can do right now on the A2AWire job board.' This clearly distinguishes it from siblings like discover_agents or get_recommended_action and explains what result the agent gets.

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

Usage Guidelines4/5

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

It says to call this now for discoverable paid work, advises filtering by capability and preferring testnet for cold-start, and names the follow-up action (start_job). It lacks an explicit 'use sibling X instead when...' exclusion, but the situational guidance is clear.

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.

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