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BPN-Solutions

VibeCheck MCP Server

by BPN-Solutions
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TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.1

  • Disambiguation5/5

    The two tools have clearly distinct purposes: check_dependencies focuses specifically on npm dependency vulnerabilities using npm audit, while scan_codebase performs a broader AI-powered security audit across multiple code aspects. There is no overlap in scope or functionality.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (check_dependencies, scan_codebase) with clear action verbs describing their functions. The naming is uniform and predictable throughout the tool set.

    Tool Count2/5

    With only 2 tools, this server feels severely under-scoped for its apparent purpose of security auditing. A comprehensive security toolset would typically include more operations like fixing vulnerabilities, monitoring, or configuration management. The count is too low for meaningful agent workflows.

    Completeness2/5

    While the tools cover vulnerability checking and code scanning, there are significant gaps in the security audit lifecycle. Missing are tools for remediation (e.g., fix_vulnerabilities, update_dependencies), reporting, configuration management, or continuous monitoring. This incomplete surface will limit agent effectiveness.

  • Average 3.9/5 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.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

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

    Then . Browse examples.

  • Add related servers to improve discoverability.

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.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    With no annotations provided, the description carries the full burden. It discloses behavioral traits like 'real-time vulnerability database lookups', 'AI reasoning and confidence scores', and the six analysis categories, which helps understand the tool's scope. However, it doesn't mention performance characteristics, rate limits, authentication requirements, or what happens during scanning (e.g., whether it modifies files).

    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 bullet points for analysis categories and return values, making it easy to scan. It's appropriately sized at 10 sentences, though some phrasing like 'AI-powered security audit' could be more precise. Every sentence adds value, but minor redundancy exists (e.g., listing categories in both text and bullets).

    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 no annotations and no output schema, the description provides good coverage of what the tool does and returns (findings with severity, reasoning, references, remediation). However, for a complex security tool with 4 parameters, it lacks details on error handling, performance, or integration constraints, leaving some contextual gaps.

    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 description coverage is 100%, so the schema already documents all four parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, such as explaining the relationship between 'path' and 'files' alternatives or providing examples. Baseline 3 is appropriate when the schema does the heavy lifting.

    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 performs an 'AI-powered security audit' with 'real-time vulnerability database lookups', specifying it 'analyzes code for' six specific vulnerability categories. It distinguishes from the sibling 'check_dependencies' by covering a broader scope including authentication, API security, secrets, and data flow issues, not 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 scanning of codebases but doesn't explicitly state when to use this tool versus the sibling 'check_dependencies'. It mentions 'alternative to path' for the files parameter, which provides some contextual guidance, but lacks explicit when/when-not rules or clear alternatives.

    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 specifies the data source (GitHub Advisory Database), describes the return format (CVEs, severity levels, patched versions), and lists important prerequisites (npm installation, lock file requirements). However, it doesn't mention potential rate limits, execution time, or error conditions.

    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, then behavioral details, then requirements. Every sentence earns its place with zero wasted words, and the information is front-loaded appropriately.

    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 does well by explaining what the tool returns and its prerequisites. However, it could be more complete by describing error conditions, typical execution time, or providing examples of the output format since there's no output schema to reference.

    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 any meaningful parameter semantics beyond what's in the schema - it mentions lock file requirements but doesn't connect them to the 'path' parameter or provide additional context about the boolean flag.

    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 ('Run npm audit', 'check dependencies for known vulnerabilities') and distinguishes it from the sibling tool 'scan_codebase' by focusing specifically on dependency vulnerability checking rather than general codebase scanning.

    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 (checking dependencies for vulnerabilities) and includes explicit requirements (npm installed, lock file present), but doesn't explicitly state when NOT to use it or mention alternatives to the sibling tool beyond the implicit distinction.

    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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