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Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'check_vulnerabilities' has a clear, distinct purpose focused on security vulnerability scanning.

    Naming Consistency5/5

    The tool name 'check_vulnerabilities' follows a consistent verb_noun pattern, and since there is only one tool, there is no inconsistency to evaluate. The naming is clear and appropriate for its function.

    Tool Count2/5

    A single tool for a security vulnerability scanning server is too limited in scope. While the tool is well-described, a server with only one tool often feels thin and lacks the breadth needed for comprehensive security operations, such as managing scans, updating databases, or handling different file types beyond the mentioned ones.

    Completeness2/5

    The tool surface is severely incomplete for a security vulnerability domain. It only covers scanning for vulnerabilities in specific dependency files, missing essential operations like configuring scan settings, retrieving historical scan results, updating vulnerability databases, or integrating with other security tools. This creates significant gaps that could lead to agent failures in broader security workflows.

  • Average 4.3/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 0 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.

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior by explaining the different scan modes ('full', 'summary', 'critical-high-only') and their outputs, and mentions it uses the OSV.dev database. However, it lacks details on potential side effects, error handling, or performance characteristics.

    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 well-structured and front-loaded with the core purpose, followed by usage guidelines. It is appropriately sized, but could be slightly more concise by integrating the usage scenarios more tightly with the initial explanation.

    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's complexity (3 parameters, no output schema, no annotations), the description does a good job covering purpose, usage, and behavior. However, it lacks information on output format or error handling, which would be helpful for an AI agent to understand what to expect from the tool's execution.

    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 schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds some context by mentioning 'project dependencies' and 'multiple scan modes', but does not provide additional semantic details beyond what the schema specifies. This meets the baseline for high schema coverage.

    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 purpose with specific verbs ('scans project dependencies') and resources ('package.json, composer.json'), and distinguishes its function by mentioning the OSV.dev database. It explicitly lists what it does without being tautological or vague.

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

    Usage Guidelines5/5

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

    The description provides explicit guidance on when to use the tool, listing specific scenarios such as 'user asks about security/vulnerabilities', 'after package installations', 'before commits/builds', and 'when starting work in a new project with dependency files'. This gives clear context for usage without alternatives needed since no sibling tools exist.

    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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