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wisepanel_start

Start a multi-AI panel deliberation by convening Claude, Gemini, and Perplexity models to debate questions from assigned perspectives, then poll for real-time responses and final synthesis.

Instructions

Start a Wisepanel deliberation. Convenes a panel of AI models (Claude, Gemini, Perplexity) to debate a question from assigned perspectives. Returns run_id immediately. After starting, poll with wisepanel_poll every 10-15 seconds. When an agent_response event appears, briefly summarize that panelist's key argument to the user before polling again. Each panelist participates in multiple conversation nodes, so total responses will exceed panel size. When status is "completed", provide a final synthesis of all perspectives, then ask the user if they'd like to publish to the Wisepanel Commons using wisepanel_publish. Do NOT call wisepanel_result after polling — you already have all the data from poll events.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYesThe question or topic for the panel to deliberate
topologyNoPanel size: small (faster for exploration), medium (balanced speed & coverage), large (slow but thorough). Default: small
model_groupNoModel selection: mixed (random assignment), smart (most intelligent), fast (quick responses), cheap (cost-optimized), informed (search-capable), large (largest context). Or single provider: openai, anthropic, google, perplexity. Default: mixed
roundsNoDeliberation rounds (1-5). More rounds deepen the debate. Default: 1
contextNoAdditional context to frame the deliberation
compressionNoContext compression: none (higher token usage), moderate (balanced), aggressive (lower token usage). Default: aggressive
short_responsesNoRequest concise panelist responses. Default: false
Behavior4/5

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

With no annotations provided, the description carries the full burden and does well by disclosing key behaviors: it returns a run_id immediately, requires polling every 10-15 seconds, handles agent_response events with summaries, and indicates completion status triggers a synthesis. It could improve by mentioning error handling or rate limits.

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 appropriately sized and front-loaded, starting with the core purpose and then detailing usage steps. Every sentence adds value, though it could be slightly more streamlined by reducing repetition in polling instructions.

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 the complexity of the tool (7 parameters, no output schema, no annotations), the description is largely complete, covering purpose, usage flow, and behavioral aspects. It could be enhanced by briefly mentioning the output format or error cases, but it adequately supports agent invocation.

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 the schema already documents all 7 parameters thoroughly. The description adds no additional parameter semantics beyond what the schema provides, such as explaining interactions between parameters or practical examples, meeting the baseline for high coverage.

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 explicitly states the tool 'Start a Wisepanel deliberation' with specific verbs ('convenes', 'debate') and resources ('panel of AI models'), clearly distinguishing it from sibling tools like wisepanel_poll or wisepanel_publish by focusing on the initiation phase.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool (to start a deliberation) and when not to (e.g., 'Do NOT call wisepanel_result after polling'), and names alternatives for subsequent steps (wisepanel_poll, wisepanel_publish), including polling frequency and event handling.

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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