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qwertymuzaffar

mcp-udacity-commit

by qwertymuzaffar
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Server Quality Checklist

67%
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  • Latest release: v1.2.2

  • Disambiguation5/5

    Each tool targets a distinct aspect of commit conventions: branch name validation, commit message validation, and commit message formatting. There is no overlap in their purposes, making selection unambiguous.

    Naming Consistency5/5

    All tool names follow the same verb_noun pattern: validate_branch_name, validate_commit_message, format_commit_message. This consistency makes the toolset predictable and easy to navigate.

    Tool Count5/5

    With only 3 tools, the server is tightly scoped to its purpose of enforcing Udacity commit guidelines. Each tool earns its place, and the count is neither too thin nor overly heavy.

    Completeness5/5

    The server covers the full lifecycle of commit standard compliance: validating branch names, validating commit messages, and formatting commit messages. There are no obvious gaps in the domain.

  • Average 3.9/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 15 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

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

    Then . Browse examples.

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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

  • Behavior3/5

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

    Description discloses returns: compliance status, problems (violations), warnings (hints). With no annotations, this is adequate but fails to mention side-effects (likely none) or response structure beyond vague categories.

    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 sentences, front-loaded with action and resource. No extraneous information.

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

    Completeness4/5

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

    Given low complexity (1 param, output schema exists), description adequately conveys purpose and returns. Missing explicit mention that output structure is defined by schema, but not critical.

    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 covers 100% of the single parameter with description 'The full commit message to check'. Tool description adds no additional semantic detail beyond schema, meeting baseline.

    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?

    Description clearly states 'check a commit message against the Udacity Git Commit Message Style Guide', specifying verb and resource. Sibling tool 'format_commit_message' suggests different operation, but no direct differentiation is made.

    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 on when to use this tool versus alternatives (e.g., format_commit_message). No prerequisites or exclusions are mentioned.

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

  • Behavior3/5

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

    The description discloses formatting behaviors (capitalization, period removal, body wrapping) but omits other behavioral details such as handling of missing optional fields, error conditions, or whether existing formatting is overridden. With no annotations, the description should cover more.

    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 concise with two sentences, no unnecessary words. It front-loads the main purpose and efficiently communicates the key formatting rules.

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

    Completeness4/5

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

    Given the tool has an output schema (likely describing the formatted string), the description need not cover returns. It adequately explains input formatting, though it could mention that type must be from the enum. Still, it is mostly complete for its simple task.

    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?

    The description adds value beyond the schema by explaining that subject is auto-capitalized and trailing period removed, and body is auto-wrapped. Schema description coverage is 75%, so the baseline is 3; the extra semantics raise it to 4.

    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 composes a Udacity-style commit message from parts, specifying key formatting actions (capitalization, period removal, wrapping). It distinctively contrasts with the sibling validate_commit_message by focusing on composition rather than validation.

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

    Usage Guidelines3/5

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

    The description does not explicitly provide when or when-not to use this tool versus the sibling. The purpose is clear, but no alternatives or exclusions are mentioned, leaving the agent to infer usage context.

    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 burden and does a good job: it discloses the return format (compliant, problems, warnings) and the base branch exemption. It does not mention that this is a read-only operation, but the verb 'Check' and the nature of validation make that evident. No contradictions.

    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 two sentences, front-loaded with the core verb and resource, and every sentence adds value. The example and return behavior are condensed effectively with no waste.

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

    Completeness4/5

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

    For a single-parameter validation tool with an output schema, the description covers purpose, compliance criteria, return contents, and exceptions. It doesn't spell out what 'kebab-case' means, but the example is sufficient. Overall complete for its complexity.

    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 a clear description for 'name', but the tool description adds extra meaning by explaining the convention and giving an example ('feat/add-dark-mode'), which helps the agent understand what values are appropriate beyond the 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 'Check' and a clear resource 'git branch name', and identifies the exact convention being validated. It naturally distinguishes itself from sibling tools that deal with commit messages (validate_commit_message, format_commit_message).

    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 implies it is the tool to use for branch name validation against the companion convention, and mentions base branch exceptions. However, it does not explicitly state when to avoid it or name alternative tools for similar tasks, though the sibling context makes the domain distinction obvious.

    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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