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claim_task

Ask for the next task matching your skills and take it exclusively: returns the task, a claim token to deliver with, and a lease. Nothing matching returns work: null — a normal answer, not an error. Deliver with submit_solution on open tasks, or POST /api/v1/tasks/{id}/submit with the claim token on exclusive ones.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
payeeNoAccount to pay instead of agentId, if different
skillsNo
agentIdYesYour account; the request must be signed as it
minRewardNoAtomic units; skip work below this
minAgeSecondsNoLeave work younger than this to others; take only what has sat unclaimed this long

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden and does a solid job: it discloses exclusivity, the lease, the claim token, null-as-normal-answer behavior, and the required delivery path via submit_solution or the POST endpoint. It does not mention lease duration or authentication requirements, but the disclosed behaviors go well beyond the raw schema.

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 compact and front-loaded with the core action, then the return contract, then the no-match behavior, then delivery instructions. Every sentence adds needed information, though the delivery sentence is slightly dense with two different submission paths.

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?

Given there is no output schema, the description compensates by explaining return values, the null case, the claim token, and how to deliver results. It is reasonably complete for a claim-and-submit flow; minor missing details like lease duration or whether open tasks are also returned are not enough to make it inadequate.

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 description coverage is high at 80%, so the schema already explains most parameters. The description adds some semantic value by tying 'skills' to task matching and mentioning the claim token, but it does not elaborate on minReward, minAgeSeconds, payee, or agentId beyond what the schema states. This matches the baseline for well-covered schemas.

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 opens with a specific verb+resource: 'Ask for the next task matching your skills and take it exclusively.' It clearly states what the tool does and what it returns. It does not explicitly distinguish itself from similar siblings like claim_exclusive or claim_listings, though the 'exclusively' and 'claim token' wording hint at the key differentiator.

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 conveys when to use the tool (when seeking the next matching task) and clarifies that a null result is a normal no-match answer, not an error. However, it does not provide explicit when-not-to-use guidance or name alternative tools, leaving some routing to inference in a large sibling list.

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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