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philiphess1

VibeCheck MCP Server

by philiphess1
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: check_dependencies focuses specifically on dependency vulnerabilities via npm audit, while scan_codebase performs a comprehensive AI-powered security audit across multiple code aspects. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (check_dependencies and scan_codebase) with clear, descriptive names that accurately reflect their functions. The naming style is uniform throughout.

    Tool Count2/5

    With only 2 tools, the server feels too thin for its apparent scope of security auditing. While the tools cover dependency scanning and codebase analysis, a security-focused server would typically benefit from more granular tools (e.g., for specific vulnerability types, remediation actions, or report generation).

    Completeness3/5

    The tools provide good coverage for vulnerability detection (dependencies and code), but there are notable gaps in the security lifecycle. Missing are tools for remediation (e.g., apply_fixes, update_dependencies), reporting (e.g., generate_report), or configuration management, which limits agent workflows to detection-only scenarios.

  • 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
    • 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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'real-time vulnerability database lookups' and lists what the scan analyzes and returns, but doesn't disclose important behavioral traits like whether this is a read-only operation, performance characteristics, rate limits, authentication requirements, or what happens when scanning large codebases.

    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 with clear sections for what it analyzes and what it returns. It's appropriately sized for the tool's complexity, though the bulleted lists could be slightly more concise. Every sentence adds value without 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?

    For a 4-parameter security scanning tool with no annotations and no output schema, the description provides good context about what vulnerabilities are checked and what information is returned. However, it lacks details about behavioral characteristics, error conditions, and the format/structure of returned findings that would be important for an AI agent to use this tool effectively.

    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?

    With 100% schema description coverage, the baseline is 3. The description doesn't add specific parameter semantics beyond what's already documented in the schema, though it provides context about what the tool analyzes which relates to the 'categories' parameter. No additional syntax, format, or usage details are provided for parameters.

    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 as an 'AI-powered security audit' that 'analyzes code' for specific vulnerability types and 'returns findings' with detailed information. It distinguishes from the sibling tool 'check_dependencies' by covering a broader range of security issues beyond just dependencies.

    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 usage for security auditing but doesn't explicitly state when to use this tool versus alternatives. While it distinguishes from 'check_dependencies' by covering more categories, it doesn't provide guidance on prerequisites, when not to use it, or comparisons to other security tools.

    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 provided, the description carries full burden and does well by disclosing key behavioral traits: it explains what data source is used ('GitHub Advisory Database'), what information is returned ('CVEs, severity levels, and patched versions'), and important prerequisites. It doesn't mention rate limits, authentication needs, or potential side effects, but provides substantial operational context.

    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 perfectly structured and concise: purpose statement first, followed by data source clarification, return values, and prerequisites in a clear bullet format. Every sentence earns its place with zero wasted words.

    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 tool with no annotations and no output schema, the description provides strong context about behavior, data source, and prerequisites. It could be more complete by describing the output format in more detail or mentioning error conditions, but covers the essential operational context well given the complexity.

    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?

    With 100% schema description coverage, the schema already documents both parameters thoroughly. The description doesn't add meaningful parameter semantics beyond what's in the schema, so it meets the baseline of 3. The description mentions lock file requirements but doesn't elaborate on parameter implications.

    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 specific action ('Run npm audit'), target resource ('dependencies'), and purpose ('check dependencies for known vulnerabilities'). It distinguishes from the sibling tool 'scan_codebase' by focusing specifically on dependency vulnerability scanning rather than general codebase analysis.

    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 provides clear context for when to use this tool by specifying prerequisites ('npm must be installed', 'Directory must contain package-lock.json'), but doesn't explicitly state when NOT to use it or mention alternatives to the sibling 'scan_codebase' tool.

    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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