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hasarahman

DepHealth MCP

by hasarahman
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Python
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: audit_project scans entire project manifests for vulnerabilities, while check_package assesses a single package before adding/upgrading. No overlap in functionality.

    Naming Consistency5/5

    Both tool names follow the consistent verb_noun pattern using snake_case (audit_project, check_package), making them predictable and easy to understand.

    Tool Count4/5

    With only 2 tools, the server is minimal but well-scoped for dependency health auditing. While additional tools could be added, the current set covers the core use cases.

    Completeness4/5

    The tools cover the primary needs: scanning a project and checking a single package. Missing operations like updating dependencies or listing all vulnerabilities are minor gaps that agents can work around.

  • Average 3.9/5 across 2 of 2 tools scored.

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

    • 0 of 1 community issues answered or closed 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.

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  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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  ]
}

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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?

    With no annotations provided, the description carries the full burden. It discloses that the tool scans manifests and queries the OSV.dev database (implying network usage), but does not state prerequisites (e.g., file access), potential failures, 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.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is two sentences with zero waste. It first defines the action and scope, then provides usage context. Every word serves a purpose.

    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 output schema exists, the agent can infer return format. The description covers input, purpose, and context. Minor gaps: no mention of error handling (e.g., if path is invalid or manifests missing).

    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% as the only parameter 'path' has a description. The tool description reemphasizes that the path should point to a project directory containing manifests, but does not add new semantic meaning beyond the schema.

    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 that the tool scans project dependency manifests and reports vulnerabilities from OSV.dev, specifying which manifest files are checked. However, it does not explicitly differentiate from the sibling tool check_package, which likely focuses on a single 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 provides good usage guidance: 'Use before deploying, reviewing, or adopting a codebase.' However, it does not mention when not to use it or suggest alternative tools like check_package for single-package checks.

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

  • 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 data sources (OSV.dev, deps.dev) and the verdict range, but does not explicitly state that the tool is read-only, whether it modifies anything, or potential side effects. The description is adequate but not comprehensive.

    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 concise with two sentences, no filler. It front-loads the core action ('Assess one package before adding or upgrading it') and then lists specifics. Every sentence 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?

    Given the 3-parameter schema and the existence of an output schema, the description covers the tool's function and outputs effectively. It lacks details about error handling or behavior when 'version' is omitted, but the schema clarifies options. Overall, it is sufficiently complete for agent understanding.

    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 67% (2 of 3 parameters have descriptions). The tool description provides context for the overall task but does not add significant detail beyond what the schema already provides for 'name' and 'version'. The 'ecosystem' parameter lacks a schema description and is not elaborated in the tool description, so value is marginal.

    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 uses a specific verb ('Assess') and resource ('one package') and clearly lists the assessments performed (vulnerabilities, health signals, version, license, release recency, deprecation) and the verdict output. It distinguishes from the sibling tool 'audit_project' by focusing on a single package rather than a project.

    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 states when to use the tool ('before adding or upgrading it'), providing clear context. It does not explicitly mention when not to use it or contrast with the sibling, but the single-package focus implicitly guides selection.

    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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