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Have recruits discuss a topic with each other

discuss

Run a structured, multi-round round-robin debate between AI recruits. Each round passes attributed replies back for pushback and refinement, returning a full transcript grouped by round.

Instructions

Round-robin discussion between two or more recruits. Round 1 is each recruit's opening position; every later round hands each of them the previous round's replies, attributed by name, and asks them to push back or refine. Returns the full transcript grouped by round.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
namesYestwo or more recruit handles
topicYeswhat they are discussing, stated once, verbatim from the user where possible
digestNoinclude the channel digest in round 1 (default true)
roundsNohow many rounds (default 2, max 5) — each round costs one call per recruit
Install Server

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden, and it does disclose the core behavioral traits well: round structure, name attribution, push-back/refine behavior, and a round-grouped transcript. However, it stays silent on side effects (does the discussion persist or mutate recruit state?) and on cost implications, even though the schema's rounds parameter notes 'each round costs one call per recruit' — that cost signal is not echoed in the description.

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 sentences, zero waste: what the tool is, how the rounds mechanically work, and what it returns. The core concept is front-loaded in the first clause, and every subsequent clause adds a distinct piece of information without repetition.

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

Completeness3/5

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

For a 4-parameter tool with no annotations and no output schema, the description covers the interaction flow competently and gives one useful return-value hint ('transcript grouped by round'). But it leaves gaps an agent would want filled: cost scaling per round, whether the discussion affects recruit state, and what the transcript entries actually look like. Adequate, but with clear holes.

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 the baseline is 3, but the description adds real meaning beyond the terse property text: it explains what happens across rounds, which clarifies the rounds parameter, and it frames names as a multi-party requirement ('two or more recruits'). It also hints at the return shape via 'full transcript grouped by round,' which is not in the schema.

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 names a specific verb and resource ('Round-robin discussion between two or more recruits') and then nails down the exact interaction pattern: Round 1 openings, later rounds handing attributed previous replies for push-back or refinement. This mechanism is specific enough to distinguish it from sibling tools like ask (single Q&A) or audition (solo evaluation), even though no sibling is named.

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?

Usage context is implied by the mechanism — an agent can infer 'use this when multiple recruits should exchange viewpoints across rounds' — but there is no explicit when-to-use statement, no named alternative, and no exclusion criteria. The description never tells the agent when to pick discuss over ask or evaluate_role, leaving selection to inference.

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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