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Agostynah

gitlab-duo-mcp-bridge

by Agostynah
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Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no ambiguity at all. An agent can clearly identify the single purpose.

    Naming Consistency5/5

    With a single tool, there are no inconsistencies or mixing of conventions. The name 'duo_review' is clear and follows a verb_noun pattern.

    Tool Count2/5

    The server claims to be a bridge for GitLab Duo, which likely offers multiple features, yet only one tool is provided. This under-scoping makes the count too low for the apparent domain.

    Completeness2/5

    The single tool covers only code review, missing other expected Duo functionalities such as code suggestions, summarization, or issue creation. This leaves significant gaps for agent workflows.

  • Average 4.5/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 8 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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}

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior4/5

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

    The description discloses fault-tolerant behavior: if Duo responds with prose instead of JSON, it returns degraded: true with raw text. No annotations exist, so the burden is fully met. No other behavioral traits (e.g., permissions) are needed for a read-like review tool.

    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 five sentences, front-loaded with the core action, and every sentence adds meaningful detail without verbosity. It is well-structured and efficient.

    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 7 parameters (none required), a complex output, and no annotations, the description covers all key aspects: input options, output structure (summary, issues), fallback behavior (degraded), and actionable use. It is fully self-contained.

    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 baseline is 3. The description adds value by explaining how parameters interact (e.g., 'pass a unified diff (and/or files/instructions)') and that the 'goal' parameter overrides the entire prompt. This clarifies usage beyond schema 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 it runs a code review with GitLab Duo CLI and returns a normalized, fault-tolerant result. It specifies inputs (diff, files, instructions) and outputs (summary, issues list), making the purpose unmistakable.

    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?

    It explicitly says 'use this to get a second opinion review', providing clear context. While no alternatives or exclusions are mentioned due to no siblings, the usage guidance is sufficient for typical scenarios.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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