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minhtzy

AI Delivery MCP

by minhtzy
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Server Quality Checklist

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Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a distinct phase of the delivery workflow: prepare creates a preview without side effects, compose generates a Jira update after a merge, and finalize persists results. There is no overlap in their purposes or expected inputs.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (prepare, compose, finalize) with clear action-first naming. The style is uniform and predictable.

    Tool Count4/5

    With three tools, the server is slightly lean but well-scoped to the core delivery lifecycle. Each tool is necessary and the count feels reasonable for the narrow, focused purpose.

    Completeness4/5

    The workflow covers prepare, compose, and finalize, which form a complete pipeline from preview to persistence. Minor gaps like status checks or cancellation are absent but not critical to the primary delivery flow.

  • Average 2.8/5 across 3 of 3 tools scored. Lowest: 1.8/5.

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

    • No community issues in the last 6 months
    • 36 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

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  • This repository includes a README.md file.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

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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

  • Behavior1/5

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

    With no annotations provided, the description must disclose behavior, but it does not. It doesn't say whether the tool posts to Jira, requires authentication, has side effects, or merely generates text. The user is left completely in the dark about consequences.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness2/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single sentence, but it is under-specified rather than concise. It omits essential details about parameters, behavior, and output. The brevity does not serve the agent's need for actionable information.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness1/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    The tool has 3 parameters, no annotations, no output schema, and sibling tools, yet the description provides almost no context. It fails to explain the output, side effects, parameter relationships, or how this fits into the delivery workflow.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters1/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, and the description mentions no parameter names or meanings. The schema only provides types, so the description must compensate, but it adds nothing about runId, transition, or mergeRequestUrl.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states a specific verb ('render') and resource ('a Jira update'), but the noun 'update' is ambiguous—it could mean a comment, a status transition, or something else. It does not explicitly distinguish this from sibling tools, though the names suggest different purposes.

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

    Usage Guidelines2/5

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

    The condition 'after a real GitLab merge request exists' implies a usage context, but there is no guidance on when to use this tool versus the sibling tools 'prepare_delivery' or 'finalize_delivery'. No exclusions or alternatives are mentioned.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It only states 'persist,' which implies a write operation, but gives no detail about side effects, idempotency, required permissions, or what 'sanitized evidence' means in practice.

    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 a single, concise sentence with no wasted words. It is easily scannable, though it is somewhat under-specified rather than elegantly concise.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool has three required parameters, nested objects, no output schema, and no annotations, the description is not sufficient. It does not explain parameter meanings, return behavior, or side effects, leaving the agent with significant ambiguity.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters1/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, and the description does not mention any of the three required parameters (runId, mergeRequest, jiraUpdate). It fails to compensate for the schema's lack of descriptions, especially since jiraUpdate is a self-referential structure that may be confusing.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description uses a specific verb 'Persist' and names the resources: 'final GitLab and Jira results, release notes, and sanitized evidence.' This clearly states the tool's purpose and implies finalization, though it does not explicitly contrast with sibling tools like prepare_delivery or compose_jira_update.

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

    Usage Guidelines2/5

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

    No guidance is given for when to use this tool versus alternatives. The word 'final' suggests it should be used after preparation or composition, but there is no explicit usage context, prerequisites, or exclusion criteria.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

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

    With no annotations, the description carries the full burden. It discloses the key behavioral trait of being side-effect-free, which is crucial for safety. However, it does not detail the input/output behavior beyond 'preview', such as what the preview contains or any environmental requirements.

    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 a single, concise sentence that front-loads the essential purpose and safety trait. Every word adds value, with no filler or repetition.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Despite the tool's complexity (10 parameters, nested objects, no output schema), the description is extremely brief. It omits details about how parameters interact, what outputs to expect, and specific usage scenarios. This leaves significant gaps for an agent to correctly invoke the tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, so the description must compensate for the lack of parameter explanations. It vaguely references 'local git' and 'Jira context', but does not map to any of the 10 parameters. The agent is left to infer meanings from parameter names alone, which is insufficient.

    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 function: 'Prepare a side-effect-free delivery preview from local git and supplied Jira context.' It uses specific verbs and resources, and distinguishes itself from sibling tools by focusing on preparation and preview, not finalization or Jira updates.

    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 implicitly indicates this tool is for previewing deliveries before finalization, and clarifies it is side-effect-free. It provides clear context but does not explicitly state when not to use it or name alternatives, so it falls short of a 5.

    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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  • 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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