Package Version Check MCP
P

Package Version Check MCP

An MCP server used to obtain the latest stable versions of dependent packages in multiple ecosystems (such as Python, NPM, Go, GitHub Actions, etc.) and the latest versions of nearly a thousand development tools (such as Python, Node.js, kubectl, Terraform, etc.), helping AI programming assistants avoid using outdated dependency versions when generating code.
2.5 points
4.7K

What is Package Version Check MCP?

Package Version Check MCP is a tool server specifically designed for AI coding assistants. It can query the latest version information of various programming language package managers (such as NPM, PyPI, NuGet, etc.) and development tools (such as Terraform, kubectl, Gradle, etc.) in real - time. When an AI assistant generates code, it can call this MCP to obtain the latest dependency versions instead of using the possibly outdated versions in the training data.

How to use Package Version Check MCP?

You can use this MCP in three ways: 1) Use the free hosted service (the simplest); 2) Run it locally using uvx; 3) Run it using a Docker container. After configuring the MCP server, clearly instruct the AI assistant in the prompt to use the MCP tool to obtain the latest version information.

Applicable scenarios

This MCP is particularly suitable for the following scenarios: When an AI assistant generates dependency files such as package.json, requirements.txt, pom.xml, etc.; When you need to update the dependency versions in an existing project; When specifying tool versions in a Dockerfile or CI/CD configuration; When you need to ensure that the generated code uses the latest security patches and features.

Main features

Multi - ecosystem support
Supports more than 12 development ecosystems, including NPM (Node.js), PyPI (Python), NuGet (.NET), Maven/Gradle (Java), Go, PHP, RubyGems, Rust, Swift, Dart, Docker, Helm, Terraform, etc.
Development tool version query
Through mise - en - place integration, it supports querying the latest versions of nearly 1000 development tools, including runtimes and tools such as Terraform, kubectl, Gradle, Maven, Node.js, Python, etc.
GitHub Actions metadata
It can not only obtain the latest version of GitHub Actions but also get the complete metadata such as its input parameters, output parameters, and execution configuration, optionally including the README usage instructions.
Flexible deployment options
It provides three usage methods: free hosted service, local uvx running, and Docker container running to meet the needs and security requirements of different users.
Intelligent caching mechanism
A configurable caching system that reduces API calls to package registries, improves response speed, and supports TTL and maximum cache size configuration.
Docker tag pattern matching
For Docker images, it supports finding the latest matching tag based on a tag pattern (such as "1.36 - alpine"), not just semantic versions.
Advantages
Extensive ecosystem coverage: Supports more than 12 package managers and nearly 1000 tools, with a much wider coverage than similar tools.
Real - time version information: Obtains the latest versions directly from official registries to ensure the accuracy and timeliness of information.
Multiple deployment methods: Provides three options: hosted service, local running, and Docker container, suitable for different usage scenarios.
Production - ready: Complete test coverage, automated dependency updates, security - hardened Docker images, and SBOM support.
Free hosted service: Provides a free online service, allowing you to use core functions without self - deployment.
Seamless integration with AI assistants: Specifically designed for AI coding assistants, with a simple and easy - to - use tool interface.
Limitations
Requires explicit prompts: AI assistants will not automatically call the MCP tool and need to be explicitly instructed in the prompt to use it.
GitHub API limitations: Querying GitHub Actions may be subject to API rate limits. It is recommended to configure the GITHUB_PAT token.
Cache reset: The in - memory cache will be cleared when the server restarts. Persistent caching needs to be implemented by yourself.
Specific format requirements: Package names in different ecosystems require specific formats, and parameters need to be provided correctly.
Tool dependencies: Local running requires installing the mise binary file to use the tool version query function.

How to use

Select a deployment method
Select a deployment method according to your needs: Use the free hosted service (the simplest), run it locally using uvx, or run it using a Docker container.
Configure the AI assistant
Add the MCP server connection information to the configuration of your AI assistant (such as Claude Desktop, Cursor, etc.).
Enable the MCP tool
Ensure that the tool provided by this MCP is enabled in the AI assistant's configuration. Different AI assistants may have different enabling methods.
Indicate usage in the prompt
Clearly instruct the AI assistant in the prompt to use the MCP tool to obtain the latest version information.
Optional: Configure environment variables
If you need to query GitHub Actions, it is recommended to set the GITHUB_PAT environment variable to avoid API limitations.

Usage cases

Create a new Node.js project
When an AI assistant needs to create a package.json file for a new Node.js project, it can use the MCP to obtain the latest versions of all dependencies instead of using the possibly outdated versions in the training data.
Update dependencies in an existing project
When you find that the code previously generated by the AI assistant uses outdated dependency versions, you can ask it to use the MCP to update to the latest versions.
Configure a CI/CD pipeline
When creating a GitHub Actions workflow, you need to specify the versions of actions and tools.
Write a Dockerfile
When writing a Dockerfile, you need to specify the base image version and the versions of installed tools.

Frequently Asked Questions

Why doesn't the AI assistant automatically use this MCP?
What are the limitations of the free hosted service?
How to query Docker tags for specific versions?
Does the MCP support private package repositories?
How to configure the cache?
What should I do if the MCP returns an error?

Related resources

GitHub repository
Source code, issue tracking, and contribution guidelines for the project
MCP official documentation
Official documentation and specifications for the Model Context Protocol
mise - en - place tool
The mise - en - place project for tool version management
Context7 MCP
The Context7 MCP service used in conjunction with this MCP
Docker image
The Docker container image for the project
PyPI package page
The project page on the Python Package Index

Installation

Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.

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