Skip to main content
Glama
Anicodeth

dependency-audit-mcp

by Anicodeth
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct scopes: audit_dependencies evaluates an entire package.json against the registry, while check_package focuses on a single package. There is no ambiguity about which tool to use for a given request.

    Naming Consistency5/5

    Both tools follow the same verb_noun pattern with lowercase and underscores: 'audit_dependencies' and 'check_package'. The naming is consistent and predictable.

    Tool Count3/5

    With only two tools, the server feels thin for a general dependency-audit purpose, but it does cover the two primary use cases: whole-project audit and single-package lookup. The count is borderline but not unreasonable for such a narrow domain.

    Completeness4/5

    The core auditing operations are covered: checking a full dependency set and checking an individual package. Minor gaps exist, such as lack of support for lockfiles or batch version comparisons, but these are workarounds and not severe.

  • Average 4.2/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 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?

    No annotations are provided, so the description carries full burden. It discloses that the tool queries the npm registry and produces a report with specific fields. However, it doesn't state whether the tool is read-only, potential rate limits, or failure modes. The 'real registry facts' line adds rationale rather than behavioral detail.

    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 exactly two sentences with no filler. The first sentence front-loads the core purpose and outputs. The second sentence provides clear usage guidance and input preparation. Every word 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?

    Without an output schema, the description adequately lists the return fields (patch/minor/major, versions behind, deprecated, license) and gives strong usage context. It could mention edge cases (e.g., invalid package.json, registry unavailability) or error handling, but for an audit tool the key information is present.

    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 each parameter described. The description adds value by directing the agent to read the repo's package.json and pass it as the `packageJson` parameter, which clarifies the expected input. It doesn't expand on includeDev/includePeer beyond the schema definitions.

    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?

    The description clearly states the tool reports each dependency's freshness against the npm registry, including how far behind (patch/minor/major), versions behind, deprecated status, and license. This is a specific verb+resource with concrete outputs. However, it does not differentiate itself from the sibling tool 'check_package'.

    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 explicitly gives trigger phrases ('update dependencies', 'check what's outdated', 'is anything deprecated') and context (before a dependency bump). It also instructs the agent to read the repo's package.json and pass its full text. It lacks exclusions or mentions of alternatives, so not a 5.

    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 full burden. It discloses the read-only nature via 'Look up' and 'reports', and lists the specific data returned (version, range lag, deprecation, license). It does not mention error behavior or rate limits, but for a simple lookup this is adequate.

    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, no wasted words. The first sentence front-loads the core purpose and outputs; the second provides usage guidance. Every phrase earns its place.

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

    Completeness5/5

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

    The tool is simple (2 params, no output schema), and the description covers all essential return values, usage context, and alternatives. It is fully self-contained for selecting and invoking the tool correctly.

    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 description coverage is 100%, but the description adds meaning by explaining how the 'range' parameter works ('how far a given range/version is behind') and provides usage examples. This exceeds the baseline of 3 by giving context 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 states a specific verb ('Look up') and resource ('one npm package'), and lists concrete outputs: latest version, range comparison, deprecation status, and license. It explicitly contrasts with 'single package' vs. the sibling 'audit_dependencies', making the distinction clear.

    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?

    It provides explicit usage context: 'Use for a quick one-off check... without needing a whole package.json.' This implies when to use this tool over a bulk audit, though it doesn't name the alternative explicitly. The exclusion ('without needing a whole package.json') gives practical guidance.

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

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

dependency-audit-mcp MCP server

Copy to your README.md:

Score Badge

dependency-audit-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Anicodeth/dependency-audit-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server