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delimit_deliberate

Run multi-model deliberation to reach consensus on critical decisions by detecting cross-model contradictions.

Instructions

Run multi-model consensus via AI-to-AI deliberation (Pro).

When to use: for foundational decisions (pricing, naming, public-facing copy framing, doctrine edits), external PR diffs, or any decision where cross-model contradiction-detection adds value. When NOT to use: for routine implementation choices (orchestrate in-thread or via subagent dispatch) — deliberation is for cross-checked confabulation, not capability.

Sibling contrast: delimit_models manages which providers can be called; this runs the actual panel. delimit_security_deliberate is the security-class variant.

Side effects: writes transcripts under save_path when provided. Models are called via configured providers; Free tier uses 3 builtin slots, Pro/Premium uses BYOK from ~/.delimit/models.json. Strategic / social scopes enforce a 3-model minimum and may invoke Grok as a tiebreaker.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYesThe question to reach consensus on. Required.
contextNoBackground context shared to all models.
modeNo"dialogue" (short turns) or "debate" (long essays). Default "dialogue".dialogue
max_roundsNoMax rounds. Default 3 for debate, 6 for dialogue.
save_pathNoOptional file path to save the full transcript.
scopeNoOptional scope override — "strategic", "social", or "operational". Empty = engine classifies from keywords.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Despite no annotations, the description thoroughly discloses behavioral traits: side effects (writes transcripts), model invocation details (configured providers, tier differences, BYOK for Pro/Premium), scope enforcements (3-model minimum, Grok tiebreaker), and modes (dialogue vs debate). This covers all necessary behavioral context.

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?

The description is concisely structured with front-loaded purpose, clear 'when to use/when not to use' sections, sibling contrast, and side effects list. Every sentence adds value without redundancy.

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

Completeness5/5

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

Given the tool's complexity (multi-model deliberation, tiers, scopes), the description covers usage guidelines, behavioral side effects, tier differences, parameter details, and sibling context. With an output schema present, return values are not needed, making the description complete.

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?

While input schema coverage is 100%, the description adds meaning beyond schema by explaining mode types ('dialogue' short turns, 'debate' long essays), default rounds per mode, scope override values ('strategic', 'social', 'operational'), and that empty scope triggers classification. This enriches parameter understanding.

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 states 'Run multi-model consensus via AI-to-AI deliberation (Pro)' and specifies use cases like foundational decisions, pricing, naming, public-facing copy. It distinguishes from sibling tools delimit_models and delimit_security_deliberate, making the purpose specific and unambiguous.

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 explicitly outlines when to use (foundational decisions, cross-model contradiction-detection) and when not to use (routine implementation choices). It also contrasts with sibling tools, providing clear context for selection.

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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