wehub-resource-sync
88 lines
3.0 KiB
Markdown
88 lines
3.0 KiB
Markdown
# KitOps MCP Server
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We are going to implement an MCP server to orchestrate KitOps for managing and distributing machine learning models. Agents will be able to connect to discover tools for creating, inspecting, pushing, pulling, and removing ModelKits from remote registries like Jozu Hub.
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What Makes ModelKits Different?
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While Docker containers package applications, ModelKits are purpose-built for AI/ML workflows. They solve the unique challenges AI engineers face when moving projects between environments.
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Key Advantages Over Traditional Docker:
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- Selectively unpack kits — skip pulling what you don’t need
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- Doubles as your private model registry
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- One-command deployment
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We use:
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- [KitOps](https://kitops.org/) for versioning, packaging, and distributing ML models
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- [Jozu Hub](https://jozu.ml/) as a remote registry for storing and sharing ModelKits
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- Cursor (MCP Host)
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## Set Up
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Follow these steps one by one:
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### Install Kit CLI
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Here is the documentation for downloading and installing the Kit CLI: [Kit CLI Installation](https://kitops.org/docs/cli/installation/) for your operating system.
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After installing the Kit CLI, you can verify the installation by running:
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```bash
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kit version
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```
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### Create .env File
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Create a `.env` file in the root directory of your project with the following content:
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```env
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JOZU_USERNAME=<your_jozu_hub_email>
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JOZU_PASSWORD=<your_jozu_hub_account_password>
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JOZU_NAMESPACE=<name_of_repository_in_jozu_hub>
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```
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All the values are associated with your Jozu Hub account. If you don't have a Jozu account, you can create one at [Jozu Hub](https://jozu.ml/).
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For our demo, we used this Jozu Hub ModelKit: [wine-class-prediction](https://jozu.ml/repository/sitammeur/wine-class-prediction/latest). You can also use this ModelKit for your experiments.
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### Install Dependencies
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```bash
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uv sync
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```
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## Use MCP Server
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Run the MCP server with the created configuration file as `mcp.json` either globally or in the current project directory. Here's the code of configuring MCP globally to run the server:
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```json
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{
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"mcpServers": {
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"kitops_mcp": {
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"command": "uv",
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"args": [
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"--directory",
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"/Users/akshay/Eigen/ai-engineering-hub/kitops-mcp",
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"run",
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"--with",
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"mcp",
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"server.py"
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]
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}
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}
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}
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```
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Refer to the `prompt.txt` file for some of the prompts you can use to interact with the MCP server through the MCP host - Cursor in our case.
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## 📬 Stay Updated with Our Newsletter!
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**Get a FREE Data Science eBook** 📖 with 150+ essential lessons in Data Science when you subscribe to our newsletter! Stay in the loop with the latest tutorials, insights, and exclusive resources. [Subscribe now!](https://join.dailydoseofds.com)
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[](https://join.dailydoseofds.com)
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## Contribution
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Contributions are welcome! Feel free to fork this repository and submit pull requests with your improvements.
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