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Run a multi-agent discussion

discuss

Run a multi-agent debate on a given topic with optional code context. AI participants critique and refine each other's answers over multiple rounds, then synthesize a ranked recommendation.

Instructions

Fan out a topic + code context to configured AI participants, run an N-round debate where they critique and refine each other's answers, then return a synthesized, ranked recommendation. Writes a full markdown transcript to disk.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYesThe question or decision to debate, e.g. 'Choose an order-execution strategy for a momentum stock-trading bot.'
roundsNoNumber of debate rounds. Round 1 = independent answers; rounds 2..N = critique/refine. Defaults to config.
contextNoCode, constraints, data, or background the participants should consider. Paste relevant source here.
optionsNoCandidate options/approaches to evaluate and rank. If omitted, participants propose their own.
synthesizerNoParticipant id to act as final synthesizer. Defaults to config.defaultSynthesizer.
participantsNoFilter to these participant ids. Defaults to all enabled participants in config.
writeTranscriptNoWhether to write the full markdown transcript to disk. Default true.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
roundsYes
degradedYes
consensusYes
disagreementsYes
rankedOptionsYes
synthesizerIdYes
recommendationYes
transcriptPathNo
participantsUsedYes
participantsFailedYes
Behavior4/5

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

No annotations provided, so description carries full burden. It discloses that the tool writes a transcript to disk (side effect) and outlines the debate process. Could mention permissions or error handling, but current detail is sufficient for basic understanding.

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 sentences with no redundancy. First sentence covers core process and output, second notes transcript. Every word earns its place.

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 tool with 7 parameters and an output schema, description explains workflow, output (ranked recommendation), and side effect (transcript). Lacks explicit mention of what output schema contains, but output schema covers that. Adequate given complexity.

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 100%, so baseline is 3. Description adds context by mapping 'topic+code context' to specific parameters and mentioning 'N-round debate' for rounds, but does not significantly enhance schema-provided meanings.

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 clearly states the tool runs a multi-agent debate with specific actions: fan out topic+context, run N-round debate, critique/refine, return ranked recommendation, and write transcript. Distinguishes from sibling listing tools (list_models, list_participants).

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?

Description implies use for multi-agent discussion/debate but lacks explicit guidance on when not to use or alternatives. Siblings are listing tools, so context is clear, but no direct usage constraints provided.

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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