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trw_prd_validate

Score a PRD against the V2 validation suite to determine readiness before implementation. Validates structure, content quality, compliance, and ambiguity to catch issues early.

Instructions

Score a PRD against the V2 validation suite before implementation.

Use when:

  • A PRD just landed and you need a READY / NEEDS-WORK verdict before coding.

  • You want ambiguity / completeness / traceability gates checked in one call.

Runs structure compliance, content quality, AARE-F compliance, and ambiguity analysis. Catches issues here that would otherwise cause rework.

Input:

  • prd_path: path to the PRD markdown file (required).

Output: ValidateResultDict with fields {total_score: float (0-100), quality_tier: str, grade: str, valid: bool, ambiguity_rate: float, completeness_score: float, traceability_coverage: float, improvement_suggestions: list[ImprovementSuggestionDict], failures: list[ValidationFailureDict], dimensions: list[DimensionScoreDict], path: str, sections_found: list[str], sections_expected: list[str], smell_findings: list[dict], ears_classifications: list[dict], readability: dict[str, float], section_scores: list[SectionScoreDict], effective_risk_level: str, risk_scaled: bool, status_drift_warnings: list[str], integrity_warnings: list[str], cache: dict}.

quality_tier values: "skeleton" | "draft" | "review" | "approved" (QualityTier enum; no "PRODUCTION" tier exists).

Example: trw_prd_validate(prd_path="docs/requirements-aare-f/prds/PRD-QUAL-074.md") → {"total_score": 87, "quality_tier": "approved", "grade": "A", "valid": true, "improvement_suggestions": []}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
prd_pathNo
Behavior4/5

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

No annotations are provided, so the description fully discloses that the tool runs structure compliance, content quality, AARE-F compliance, and ambiguity analysis. It also notes that it catches issues that would cause rework. It does not mention side effects, auth needs, or rate limits, but for a read-only validation tool, this is sufficient.

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 well-structured with clear sections for purpose, use cases, what it checks, input/output, and an example. It is concise yet complete, with no wasted sentences.

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 that there is no output schema, the description provides a detailed breakdown of the return structure, including enum values for quality_tier and a crucial note that no 'PRODUCTION' tier exists. This level of detail compensates for the lack of an output schema.

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?

With only one parameter and 0% schema description coverage, the description adds meaning by stating 'path to the PRD markdown file (required).' There is a minor inconsistency: the schema has a default empty string and no required field, but the description still provides useful context.

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 scores a PRD against a V2 validation suite before implementation. It distinguishes from siblings like trw_prd_create and trw_prd_diff by focusing on validation rather than creation or diffing.

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 explicitly says 'Use when: A PRD just landed...' and 'You want ambiguity / completeness / traceability gates checked in one call.' It does not mention when not to use or alternatives, but the context is clear enough for an agent to decide.

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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