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RigorLoop Research Bounties

Accept or Contest Expert Result

accept_or_contest_result
DestructiveIdempotent

Accept the submitted human-expert result for a Research Bounty created by this same agent key, triggering the existing payout workflow, or contest it with a concrete reason and open RigorLoop Platform Dispute Review.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYes
reasonNoRequired only when action is contest.
deliverable_idYes
research_bounty_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYes
dataNo
toolYes
errorNo
statusYes
requestIdYesStable RigorLoop request identifier for support and integration feedback.
nextActionNo

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate mutation (readOnlyHint=false) and destructiveHint=true. The description adds valuable context by naming the side effects: triggering the payout workflow on accept and opening a dispute review on contest. It also notes the same-agent-key prerequisite. 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?

The description is a single sentence, front-loaded with action verbs ('Accept', 'contest'), and contains no filler. Every phrase carries meaning, effectively communicating both usage modes and side effects.

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?

The description covers both actions, their side effects, the conditional requirement for a reason, and a key prerequisite (same agent key). Since an output schema exists, return values need not be described. The tool's conditional logic is adequately explained.

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 coverage is low (25%), but the description partially compensates by implying research_bounty_id refers to the bounty created by the same agent key, deliverable_id to the submitted result, and reason to contest justification. It doesn't explain constraints like minLength for reason or UUID formats, leaving some gaps.

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 dual functionality: accepting a result (triggering payout workflow) or contesting it (opening dispute review). It uses specific verbs and identifies the resource ('submitted human-expert result for a Research Bounty'), distinguishing it from sibling tools like get_submitted_result.

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?

The description provides clear context: it applies to submitted expert results on bounties created by the same agent key, and covers both accept and contest actions. However, it does not explicitly mention when not to use it or name alternative tools, stopping short of a 5.

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

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct entity or action: draft creation, funding, quote, file upload, applications, expert selection, result viewing/acceptance, status, and search. There is no overlap or confusion between tools.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern, using clear verbs like create, fund, get, list, prepare, complete, accept, select, search, and report. The pattern is uniform across the entire set.

Tool Count5/5

Twelve tools is appropriate for a research bounty platform, covering the full workflow without redundancy. Each tool has a clear role, and the count is within the ideal range.

Completeness5/5

The tool set covers the complete bounty lifecycle: draft creation, funding, file upload, applications, expert selection, status tracking, result review, and acceptance/contest. It also includes a quote and a search function for public bounties, plus a feedback channel, leaving no obvious gaps.

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