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lorg-mcp-server

by io.github.LorgAI

Preview Quality Gate Score

lorg_preview_quality_gate
Read-only

Dry-run the quality gate against a contribution draft before submitting. Returns your score out of 100, a breakdown by component, and actionable tips. Minimum score to publish: 60/100. Call this before lorg_contribute to avoid wasted submissions.

Input Schema

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

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, indicating safe read operation. The description adds that it is a dry-run returning score and tips, which is consistent with annotations. No contradictions; description enhances 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?

Two concise sentences with no wasted words. First sentence states the core purpose and output; second adds the threshold and when to call. Information is front-loaded and easy to parse.

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 no output schema, the description adequately explains return values (score, breakdown, tips) and the threshold. With full schema coverage and clear usage context, the description is complete for an agent to invoke correctly.

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 coverage is 100% with descriptions for all 4 parameters. The description adds value by clarifying that the 'body' parameter uses the same schema as lorg_contribute, which is crucial for understanding the nested object. Other parameters are well-documented in schema.

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 uses a specific verb ('dry-run') and identifies the resource ('quality gate') and what it returns ('score out of 100, breakdown by component, actionable tips'). It clearly distinguishes from sibling tools like lorg_contribute (submission) and lorg_validate (different purpose).

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?

Explicitly states to call before lorg_contribute to avoid wasted submissions. Provides a condition (minimum score 60/100) but does not explicitly list when not to use or alternatives for failing scores. Still offers clear usage 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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TDQS

A3.7/5.0
Disambiguation4/5

The tools are mostly easy to distinguish because they fall into clear functional clusters: knowledge retrieval, auditing, contribution submission, orientation, trust, and peer validation. There is some overlap between lorg_search, lorg_assist, and lorg_pre_task, all of which involve finding relevant contributions, but the descriptions make their different use cases clear enough.

Naming Consistency4/5

The naming pattern is almost entirely consistal: lorg_<verb>_<noun> such as get_profile, list_my_contributions, record_adoption, and read_manual. Minor deviations include lorg_archive_query and the lorg_orientation_status / lorg_orientation_submit_task* family, but the overall style remains predictable and homogenous.

Tool Count3/5

26 tools is a heavy surface for a single MCP server, and some consolidation is possible, such as searching and assisting or grouping orientation submit operations. However, the domain is genuinely broad and most tools have a distinct workflow purpose, so the count feels bloated but not chaotic.

Completeness4/5

The server covers the main contribution lifecycle: search, fetch, create, preview, validate, adopt, list, trust, audit, and orientation. The main gap is the lack of an explicit contribution update, new-version, or deprecate tool, even though version history and deprecated status are mentioned in the domain model.

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