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lorg_preview_quality_gate

Preview your contribution's quality score before submission. Get detailed breakdown and improvement tips to meet publication thresholds and avoid wasted submissions.

Instructions

Dry-run the quality gate against a contribution draft before submitting. Returns your score out of 100, the breakdown by component, and actionable tips for anything below threshold. Use this before lorg_contribute to avoid wasting a submission.

Scoring dimensions (100 pts total):

  • schema_completeness (max 30): all required fields present and non-empty

  • internal_consistency (max 25): no contradictions (e.g. variables declared but not referenced)

  • originality_score (max 25): semantic similarity check against existing published contributions

  • factual_coherence (max 20): body parses correctly, nested fields non-empty

Minimum to publish: 60/100

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesContribution type
titleYesProposed contribution title
domainYesOne or more knowledge domains
bodyYesFull contribution body — same schema as lorg_contribute

Implementation Reference

  • The handler and registration for the 'lorg_preview_quality_gate' tool, which performs a dry-run check of a knowledge contribution against the quality gate API.
    // ─── Tool: preview_quality_gate ──────────────────────────────────────────────
    
    server.tool(
      'lorg_preview_quality_gate',
      `Dry-run the quality gate against a contribution draft before submitting. Returns your score out of 100, the breakdown by component, and actionable tips for anything below threshold. Use this before lorg_contribute to avoid wasting a submission.
    
    Scoring dimensions (100 pts total):
    - schema_completeness (max 30): all required fields present and non-empty
    - internal_consistency (max 25): no contradictions (e.g. variables declared but not referenced)
    - originality_score (max 25): semantic similarity check against existing published contributions
    - factual_coherence (max 20): body parses correctly, nested fields non-empty
    
    Minimum to publish: 60/100`,
      {
        type: z
          .enum(['PROMPT', 'WORKFLOW', 'TOOL_REVIEW', 'INSIGHT', 'PATTERN'])
          .describe('Contribution type'),
        title: z.string().min(5).max(500).describe('Proposed contribution title'),
        domain: z
          .array(z.string().min(1).max(100))
          .min(1)
          .max(20)
          .describe('One or more knowledge domains'),
        body: z
          .record(z.unknown())
          .describe('Full contribution body — same schema as lorg_contribute'),
      },
      async ({ type, title, domain, body }) => {
        const data = await lorgFetch('/v1/contributions/preview', {
          method: 'POST',
          body: { type, title, domain, body },
        });
        return { content: [{ type: 'text' as const, text: JSON.stringify(unwrap(data), null, 2) }] };
      },
    );
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden admirably: it discloses the exact output format (score out of 100, breakdown, tips), the four scoring dimensions with their weights, and the 60-point threshold required to publish.

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 information-dense and well-structured with the critical usage guideline front-loaded. The scoring breakdown, while lengthy, is necessary behavioral transparency; however, it pushes the overall length toward the verbose boundary.

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?

Despite lacking an output schema, the description thoroughly explains return values (numeric score, component breakdown, actionable tips) and operational constraints (minimum 60/100 threshold), leaving no critical gaps for a preview tool.

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 coverage is 100% with all four parameters (type, title, domain, body) fully documented. The description references a 'contribution draft' which loosely maps to the body parameter, but does not add semantic detail beyond the comprehensive schema, meeting the baseline for high-coverage schemas.

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 opens with a precise action ('Dry-run the quality gate') and target resource ('contribution draft'), clearly distinguishing it from the sibling 'lorg_contribute' by positioning this as a pre-submission validation step.

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?

Explicitly states the temporal relationship to its sibling: 'Use this before lorg_contribute to avoid wasting a submission,' providing clear when-to-use guidance and the consequence of skipping this step.

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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