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RigorLoop Research Bounties

Select Expert

select_expert
DestructiveIdempotent

Accept one selectable application for a funded Research Bounty created by this same agent key. Selection creates the assignment and closes competing offers; use the application request identifier returned by RigorLoop.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
confirm_selectionYesMust be true to acknowledge that expert selection creates the assignment.
research_bounty_idYes
application_request_idYes

Output Schema

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

TDQS

A3.9/5.0
Behavior4/5

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

The description adds valuable context beyond the annotations by stating that selection creates an assignment and closes competing offers, which elaborates on the destructiveHint annotation. It also introduces the 'same agent key' constraint. No contradictions with annotations were found.

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 concise sentences front-load the primary action and include only essential details: the core effect, the critical constraint, and the identifier source. There is no redundancy or filler.

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?

For a selection tool with an output schema, the description covers the main behavioral effect, a key prerequisite (funded, same agent key), and the identifier source. It omits direct explanation of research_bounty_id and the required confirm_selection flag, but the schema covers the latter, leaving only a small gap.

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

Parameters2/5

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

Only application_request_id is semantically explained ('identifier returned by RigorLoop'); research_bounty_id has no description, and confirm_selection relies on the schema's const true description. With schema coverage at 33%, the description insufficiently compensates for the two undocumented parameters.

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 identifies the action ('accept one selectable application') and the resource ('for a funded Research Bounty created by this same agent key'). It distinguishes from siblings like list_bounty_applications and accept_or_contest_result by explaining the selection outcome and closing of competing offers.

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 implies the tool is used after obtaining an application request identifier from RigorLoop for a self-created, funded bounty, but it does not explicitly state when not to use it or compare with alternatives. The 'same agent key' constraint provides context, but exclusions are absent.

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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