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TweedBeetle

dependency-fitness-mcp

by TweedBeetle
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.1

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one checks a single package with cross-validated intelligence, the other audits multiple dependencies at once with a summary. No ambiguity between them.

    Naming Consistency4/5

    Both tools follow a verb_noun pattern (check_package_fitness, audit_dependencies) but use different verbs ('check' vs 'audit') and different objects. Slightly inconsistent but still predictable.

    Tool Count4/5

    With only 2 tools, the server is lean but covers the core functionality (single package check and batch audit). Could be expanded with more granular tools, but the count is reasonable for the narrow domain.

    Completeness4/5

    The server provides the main operations needed for npm dependency health checking: single-package verdict and batch audit. Lacks features like vulnerability details or update commands, but covers the essential use cases without dead ends.

  • Average 4.5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 12 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

  • Behavior3/5

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

    Discloses cap of 50 packages and output structure (verdict + summary). No annotations provided, so description carries burden; lacks details on whether it mutates anything or if it's read-only.

    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 concise sentences covering purpose, inputs, use case, and limit. No wasted words.

    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?

    With output schema present, description covers all necessary context: purpose, inputs, usage, and constraints. No gaps.

    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%, so baseline 3. Description adds concrete examples for packages and explains that package_json extracts all dependency sections, adding value beyond 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?

    Clearly describes auditing many npm dependencies and returning per-package verdicts plus summary. Distinct from sibling check_package_fitness by focusing on bulk audit.

    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?

    Explicitly states it's ideal for CI/pre-merge dependency gates and mentions 50-package cap. Could explicitly contrast with sibling but context makes it clear.

    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 full burden. It discloses source reconciliation, confidence scoring, safe replacement inference, and detection of hallucinated package names. It could be more explicit about error handling or whether the operation is read-only, but the description is fairly transparent.

    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 two sentences, front-loaded with the core purpose, and efficiently lists distinguishing features without redundancy. Every sentence adds value.

    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?

    Given the tool's complexity and the presence of an output schema, the description covers what the agent needs: a fitness verdict, confidence score, safe replacement, disagreement flag, and detection of non-existent packages. The output schema handles return value details.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 100% with both parameters described. The description adds context beyond the schema by explaining version as semver range or dist-tag and noting that omitting version checks the latest. This enriches the agent's understanding.

    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 returns a cross-validated fitness verdict for an npm package, specifying the resource and action. It distinguishes itself from a plain 'is it deprecated' lookup by mentioning reconciliation from multiple sources and detection of non-existent packages.

    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 implies use cases by contrasting with a simpler lookup and mentioning inference of safe replacements. However, it does not explicitly compare with the sibling tool 'audit_dependencies' or state when not to use this 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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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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