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debate

Orchestrates multiple AI models to provide independent answers, then facilitates a debate where models review all responses and vote, enabling multi-model consensus for robust analysis.

Instructions

Multi-model debate: Step 1 (independent answers) + Step 2 (debate/critique). Each model provides independent answer, then reviews all responses and votes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesStep name (e.g., 'Initial Analysis', 'Security Review')
contentYesYour question to the AI Assistant. Provide detailed context: your goal, what you've tried, what worked, any specific challenges. IMPORTANT: Always include paths to relevant files in `relevant_files` - do NOT skip this step.
step_numberYesCurrent step
next_actionYesRecommended next action: 'continue' to proceed, 'stop' to end
base_pathYesAbsolute path to project root to id the project and load project files
thread_idNoThread ID to continue previous conversation and preserve context. WHEN TO USE: - None/omit: Starting a brand new review or chat session (step_number=1) - Provide thread_id: Continuing a multi-step workflow from a previous response (step_number>1) The thread_id is returned in every response - save it and reuse it for follow-up steps.
relevant_filesNoAbsolute paths of ALL files relevant to this question (up to 100 files). CRITICAL: For project-level questions (features, architecture, design), you MUST include project documentation (README.md, docs/, architecture diagrams). For code-specific questions, include the implementation files, related modules, tests, and configs. Example 1: 'What feature should we build?' → Include README.md, src/server.py, config/*.*, tests/. Example 2: 'Review this function' → Include the file with the function, related modules, tests, and documentation.
modelsNoList of LLM models to run in parallel (minimum 2) (will use default models (['gpt-4', 'gpt-3.5-turbo']) if not specified)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description carries full burden. It discloses the two-step behavioral flow (independent answers then debate/voting), which is useful. However, it omits details like idempotency, side effects, or any constraints. Adequate but not thorough.

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 clear sentences, no redundant words. Front-loaded with the core concept 'Multi-model debate'. Every sentence adds value, efficiently conveying the two-step process.

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 full schema coverage and presence of an output schema, the description adequately covers the conceptual flow. It could mention the requirement of at least two models, but that is already in the schema. Minor gaps exist but overall sufficient for an agent to understand execution.

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. The tool description does not add any parameter-specific meaning beyond what the schema already provides. It focuses on the overall process rather than individual 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 explicitly states 'Multi-model debate' and outlines the two-step process (independent answers then debate/critique), which clearly sets it apart from sibling tools like chat (single model) or compare (likely pairwise comparison). The verb+resource is specific and distinct.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool vs alternatives. It describes the process but does not specify scenarios (e.g., when you need multiple perspectives vs a single answer) or provide exclusion criteria. Lacks context for appropriate use.

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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