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NY5184

dependency-health

by NY5184

MCP Dependency Health Checker

A Model Context Protocol (MCP) server that performs comprehensive health checks on project dependencies for JavaScript and Python projects.

Features

  • πŸ” Automatic Ecosystem Detection: Detects whether your project is JavaScript or Python

  • πŸ“¦ Package Manager Support:

    • JavaScript: npm-compatible (package.json)

    • Python: pip (requirements.txt)

  • πŸ”„ Real-time Registry Queries: Fetches latest versions from npm and PyPI

  • ⚠️ Outdated Dependency Detection: Compares current versions with latest releases

  • 🚨 Pre-release Detection: Identifies pre-release versions

  • πŸ“Š Detailed Status Reports: Provides comprehensive information about each dependency

  • πŸ€– LLM-First Design: Clean, text-focused output designed for optimal LLM consumption

  • πŸš€ Automatic Changelog Fetching: Fetches actual release notes from GitHub releases so LLMs can analyze specific changes, bug fixes, and breaking changes without additional web requests

  • πŸ“ Always Actionable: Every dependency includes meaningful changelog content - either actual release notes or clear explanations when fetching fails

Related MCP server: MCP Package Hero

Installation

Prerequisites

  • Python 3.11 or higher

  • uv package manager

Setup

  1. Clone the repository:

git clone https://github.com/NY5184/mcp-dependency-health.git
cd mcp-dependency-health
  1. Install dependencies:

uv sync

Usage

Running the MCP Server

Start the server in development mode:

uv run mcp dev src/server.py

Using with MCP Clients

Configure your MCP client (like Claude Desktop) to connect to this server:

{
  "mcpServers": {
    "dependency-health": {
      "command": "uv",
      "args": ["run", "src/server.py"],
      "cwd": "/path/to/mcp-dependency-health"
    }
  }
}

Available Tools

dependency_health_check

Performs a comprehensive health check on project dependencies.

Input:

  • project_path (string): Path to the project directory

  • ecosystem (string, optional): "auto", "javascript", or "python" (defaults to "auto")

Output: Returns a list of dependencies with:

  • name: Package name

  • current: Currently specified version

  • latest: Latest version available in registry

  • status: "up-to-date", "outdated", or "unknown"

  • changelog_content: Always present - contains actual release notes when successfully fetched, or an explanatory message with source link if fetching failed

  • note: Additional information (optional)

  • release_date: When the latest version was released (optional)

  • description: Short package description (optional)

Example Input:

{
  "project_path": "/path/to/your/project",
  "ecosystem": "auto"
}

Example Output:

{
  "dependencies": [
    {
      "name": "react",
      "current": "^18.0.0",
      "latest": "18.2.0",
      "status": "outdated",
      "changelog_content": "Release v18.2.0:\n\n## Fixes\n- Fix memory leak in development mode\n- Fix Suspense bug with nested components\n- Improve TypeScript definitions\n\n## Features\n- Add useId hook\n- Suspense improvements",
      "release_date": "2022-06-14T16:55:41.036Z",
      "description": "React is a JavaScript library for building user interfaces."
    }
  ]
}

LLM-First Design: The output is designed for optimal LLM consumption:

  • βœ… No URL clutter - URLs are used internally but not exposed in the output

  • βœ… Always actionable - changelog_content always contains meaningful text

  • βœ… Self-contained - LLMs can provide specific upgrade advice without additional web requests

  • βœ… Clear fallbacks - When changelog fetching fails, the content explains what happened

Example changelog_content values:

  • Success: Actual release notes from GitHub

  • Fetch failed with source: "Changelog available at: https://github.com/user/repo/releases\n\nThe release notes exist but could not be automatically extracted. Visit the URL above for full details."

  • No source found: "Changelog could not be fetched automatically. No official changelog source was found."

With this structure, an LLM can immediately provide specific advice like:

  • "React 18.2.0 includes bug fixes for Suspense and fixes a memory leak in development mode. Safe to upgrade."

  • "This release was from June 2022, so it's well-tested and stable."

Project Structure

mcp-dependency-health/
β”œβ”€β”€ main.py                      # Entry point (if needed)
β”œβ”€β”€ pyproject.toml       # Project configuration and dependencies
β”œβ”€β”€ uv.lock                     # Locked dependency versions
β”œβ”€β”€ README.md                   # Project documentation
β”‚
β”œβ”€β”€ src/                        # Main source code
β”‚   β”œβ”€β”€ __init__.py
β”‚   β”œβ”€β”€ server.py              # MCP server implementation
β”‚   └── services/              # Service layer
β”‚       β”œβ”€β”€ __init__.py
β”‚       β”œβ”€β”€ changelog_fetcher.py # Changelog content fetching
β”‚       β”œβ”€β”€ error_handlers.py  # Error handling utilities
β”‚       └── registry_clients.py # npm/PyPI registry clients
β”‚
β”œβ”€β”€ schemas/                   # Data schemas
β”‚   β”œβ”€β”€ __init__.py
β”‚   β”œβ”€β”€ input.py              # Input validation schemas
β”‚   └── output.py             # Output data schemas
β”‚
β”œβ”€β”€ utils/                    # Utility modules
β”‚   β”œβ”€β”€ __init__.py
β”‚   β”œβ”€β”€ file_finder.py       # Project file discovery
β”‚   β”œβ”€β”€ parsers.py           # Dependency file parsers
β”‚   └── versions.py          # Version comparison utilities
β”‚
└── tests/                   # Test suite
    β”œβ”€β”€ test_file_finder.py
    β”œβ”€β”€ test_parsers_js.py
    β”œβ”€β”€ test_parsers_py.py
    β”œβ”€β”€ test_registry_clients.py
    β”œβ”€β”€ test_server_sanity.py
    └── test_versions.py

Development

Running Tests

uv run pytest

Code Structure

  • FastMCP Framework: Uses FastMCP for easy MCP server creation

  • Async Operations: Leverages httpx for efficient async HTTP requests

  • Type Safety: Full type hints with Pydantic models

  • Version Parsing: Uses packaging library for semantic version comparison

Dependencies

  • fastmcp or mcp>=1.25.0: MCP server framework

  • httpx>=0.28.1: Async HTTP client

  • pydantic>=2.12.5: Data validation

  • packaging>=25.0: Version parsing and comparison

How It Works

  1. File Discovery: Scans the project directory for package.json or requirements.txt

  2. Ecosystem Detection: Automatically determines if it's a JavaScript or Python project

  3. Dependency Parsing: Extracts package names and version specifications

  4. Registry Queries: Queries npm or PyPI for the latest versions and contextual information

  5. Changelog Fetching: Automatically fetches release notes from GitHub releases (when available)

  6. Version Comparison: Compares current versions with latest releases

  7. Status Report: Returns detailed information about each dependency with actual changelog content and links

Limitations

This tool has limited package manager support. It currently only supports:

Supported Package Managers

  • JavaScript: npm (via package.json)

  • Python: pip (via requirements.txt)

Unsupported Package Managers

The following package managers are not currently supported:

  • Python: Poetry (pyproject.toml), Pipenv (Pipfile), Conda (environment.yml)

  • Rust: Cargo (Cargo.toml)

  • Go: Go modules (go.mod)

  • Java: Maven (pom.xml), Gradle (build.gradle)

  • PHP: Composer (composer.json)

  • .NET: NuGet (.csproj, packages.config)

  • Ruby: Bundler (Gemfile)

  • Other: Any other package manager not listed above

When a project uses an unsupported package manager or no supported dependency file is found, the tool will return a dependency result with:

  • status: "unknown"

  • A clear note explaining that the project uses an unsupported dependency manager and listing the currently supported ones

Troubleshooting

"Failed to canonicalize script path"

If you encounter this error:

  1. Ensure you're in the project directory

  2. Recreate the virtual environment:

    uv sync
  3. Try running directly: uv run python src/server.py

Import Errors

If you see ModuleNotFoundError:

  • Ensure all dependencies are installed: uv sync

  • Check that you're using the correct virtual environment

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Available Tools

1 tool
dependency_health_checkA

Analyzes project dependencies and provides contextual data to assess upgrade impact.

Supports: npm (package.json) and pip (requirements.txt) only. Other package managers (Poetry, Pipenv, Cargo, Go modules, Maven, Gradle, Composer, NuGet, etc.) are not supported.

Returns for each dependency: name, current version, latest version, status, changelog_content (actual release notes or explanatory message), description, and release_date.

When to use this tool:

  • User asks about dependency versions/updates in a specific project

  • User requests dependency health check or audit

  • Before suggesting upgrades that require inspecting actual project dependencies

How to analyze results:

  1. Use changelog_content to identify specific changes, bug fixes, breaking changes

  2. State that assessment is limited if changelog could not be fetched

  3. Direct user to source URL if mentioned in changelog_content

  4. Do not infer changes not explicitly stated in changelog_content

  5. Use description and release_date to assess maturity/stability

  6. Consider major version gaps as potential breaking change indicators

Provide recommendations (low/medium/high impact) based on risk, scope, and maintenance implicationsβ€”not just version differences.

ParametersJSON Schema
NameRequiredDescriptionDefault
payloadYes

TDQS

A3.5/5.0
Behavior4/5

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

No annotations are provided, so the description bears full burden. It details return fields (name, current version, etc.), supported managers, and limitations (only npm/pip). It also advises on interpreting changelog_content and not inferring unstated changes. This is thorough for a read-analysis tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

The description is long but well-structured with sections and bullet points. It front-loads the purpose and supported managers. However, the 'How to analyze results' section, while useful, adds length. It could be more concise without losing value.

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

Completeness2/5

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

The output format is described, but the input parameter 'payload' is left undefined, making the tool incomplete. Given no output schema, the description covers return fields well, but the missing input specification severely limits completeness.

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

Parameters1/5

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

The input schema has one required parameter 'payload' (an object with additionalProperties true) but schema coverage is 0%. The description does not explain what the payload should contain (e.g., project path). This is a critical omission, leaving the agent unable to correctly invoke the tool.

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 explicitly states the tool analyzes project dependencies to assess upgrade impact, lists supported managers (npm, pip), and clearly distinguishes unsupported ones. This provides a specific verb and resource with clear scope.

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?

A dedicated 'When to use this tool' section provides three specific scenarios (e.g., user asks about dependency versions, requests health check, before upgrades). However, no sibling tools are listed, so no explicit exclusions or alternatives are given.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev0.1.0
    • First observeddependency_health_check

TDQS

A3.6/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no risk of confusion between tools. The single tool's purpose is clearly defined.

Naming Consistency5/5

The tool name 'dependency_health_check' uses a clear snake_case convention, which is consistent with common naming practices. With only one tool, consistency is not an issue.

Tool Count4/5

The single tool covers the core function of dependency health checking, but the scope is narrow (only npm and pip). The count is slightly below the typical range but still reasonable for a focused utility.

Completeness2/5

The tool only supports npm and pip, leaving out many common package managers (e.g., Cargo, Go modules, Maven). This is a significant gap that limits the tool's usefulness for projects using other managers.

Maintenance

ActivityInactive
ResponsivenessNo issues

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