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ouroboros_generate_seed

Generate a structured requirements seed from a completed interview session, gated by an ambiguity score threshold unless force is used. Converts session dialogue into goal, constraints, and acceptance criteria.

Instructions

Generate an immutable Seed from a completed interview session. The seed contains structured requirements (goal, constraints, acceptance criteria) extracted from the interview conversation. Generation requires ambiguity_score <= 0.2 unless force=true is passed to deliberately bypass the gate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
forceNoBypass the ambiguity-score threshold and generate the seed even when ambiguity_score > 0.2. Mirrors the CLI 'Generate Seed anyway' opt-in: the real score is still recorded in seed metadata for provenance, and the bypass is emitted to the audit log. Defaults to false.
session_idYesInterview session ID to convert to a seed
client_gatesNoClient-side interview gates acknowledged before seed generation. Expected values include seed_ready_acceptance_guard and restate_goal_approved.
ambiguity_scoreNoAmbiguity score for the interview (0.0 = clear, 1.0 = ambiguous). Required if interview didn't calculate it. Generation fails if > 0.2.
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. It discloses key behavioral traits: the seed is immutable, generation is gated by ambiguity_score, and force=true bypasses the gate. It does not cover all side effects, but the critical gate behavior and immutability are transparently stated.

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 three sentences, each earning its place: purpose, output content, and gating condition. No fluff or repetition; it is concise and front-loaded with the core action.

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 tool's complexity (4 params, no output schema, no annotations), the description explains the essential behavior and gate. It omits potential error cases or return value details, but this is acceptable as the seed generation step is well-contextualized by the completed interview session and sibling tools.

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 description coverage is 100% so the baseline is 3. The description adds value beyond the schema by explaining the interaction between ambiguity_score and force, i.e., the threshold and bypass mechanism, which is not evident from individual parameter descriptions.

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 the tool's purpose: 'Generate an immutable Seed from a completed interview session.' It specifies the action (generate), the resource (Seed), and the source (completed interview), distinguishing it from siblings like execute_seed which consumes seeds.

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

Usage Guidelines4/5

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

The description provides clear context for when to use the tool: after an interview session is complete and when ambiguity is low (<=0.2) or when force=true is passed. It does not explicitly mention alternatives or exclusions, but the context is unmistakable and the condition adds practical guidance.

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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