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prakharsharma13

CodeVitals

by prakharsharma13
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Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    health_check and analyze_repository are clearly distinct: one checks server status, the other performs repository analysis. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (health_check, analyze_repository), making the naming predictable and uniform.

    Tool Count3/5

    With only 2 tools, the set feels thin. However, the scope is narrow and focused on repository analysis, so it is borderline rather than severely lacking.

    Completeness4/5

    The server covers its core purpose with a health check and a repository analysis tool. The analysis tool provides metadata, health score, and summary, leaving no obvious dead ends for basic usage, though deeper analysis features are absent.

  • Average 3.8/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • No commit activity data available
    • 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.

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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

  • Behavior2/5

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

    With no annotations, the description is the sole source of behavioral disclosure, but it only lists outputs. It does not state whether the tool is read-only, modifies the repository, has side effects, or requires specific permissions. This leaves significant behavioral ambiguity.

    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 efficiently states the tool's core function and deliverables. No unnecessary words or repetition.

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

    Completeness3/5

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

    Given the tool's simplicity (one parameter, no output schema), the description covers the basic input and output. However, it omits important contextual guidance such as when to use this over 'health_check', and does not clarify potential side effects or prerequisites. This is adequate but not fully complete.

    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?

    The input schema provides 100% coverage for the 'repositoryPath' parameter with a clear description ('Absolute path to the repository to analyze'). The tool description adds no additional semantic detail beyond the schema, so a baseline of 3 is appropriate.

    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 action ('Analyzes a repository') and its outputs ('metadata, a health score, and a summary'). It also differentiates from the sibling tool 'health_check' by covering a broader scope than just health, making its purpose distinct.

    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 usage guidance is provided. The description does not mention when to use this tool versus the sibling 'health_check', nor any exclusions or alternative tools. The context signal of a sibling tool is ignored.

    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?

    No annotations are provided, so the description carries the full burden. It clearly states the tool performs a read-only check of server status, which is transparent and accurate. It does not detail the return format, but for a health check the behavior is sufficiently disclosed.

    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 fully explains the tool's purpose without unnecessary words. It is front-loaded and earns its place.

    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 simple health check with no parameters and no output schema, the description is adequately complete. It could mention the expected return value (e.g., status object), but the given context makes the tool's function clear enough.

    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 tool has zero parameters, so the baseline is 4. The description adds no parameter details because there are none to document; the schema confirms this. This is appropriate for a no-input tool.

    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 ('checks') and clearly names the target resource ('CodeVitals MCP server'), making the tool's purpose unmistakable. It implicitly distinguishes from the sibling analyze_repository, which has a different function.

    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 implies the tool is used to verify server availability before performing other operations, but it does not explicitly state when or when not to use it. No alternatives are mentioned, though none are needed for a health check.

    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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