Add docs for MCP Server and VSCode Extension

docs/book/how-to/manage-zenml-server/mcp-chat-with-server.md

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+---
+description: Chat with your ZenML server
+---
+
+# Chat with your ZenML server
+
+ZenML server supports a chat interface that allows you to interact with the server using natural language through the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/). This feature enables you to query your ML pipelines, analyze performance metrics, and generate reports using conversational language instead of traditional CLI commands or dashboard interfaces.
+
+![ZenML MCP Server Overview](../../.gitbook/assets/mcp-zenml.png)
+
+## What is MCP?
+
+The Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to Large Language Models (LLMs). Think of it as a "USB-C port for AI applications" - providing a standardized way to connect AI models to different data sources and tools.
+
+MCP follows a client-server architecture where:
+- **MCP Clients**: Programs like Claude Desktop or IDEs (Cursor, Windsurf, etc.) that want to access data through MCP
+- **MCP Servers**: Lightweight programs that expose specific capabilities
+  through the standardized protocol. Our implementation is of an MCP server that connects to your ZenML server.
+
+## Why use MCP with ZenML?
+
+The ZenML MCP Server offers several advantages for developers and teams:
+
+1. **Natural Language Interaction**: Query your ZenML metadata, code and logs using conversational language instead of memorizing CLI commands or navigating dashboard interfaces.
+2. **Contextual Development**: Get insights about failing pipelines or performance metrics without switching away from your development environment.
+3. **Accessible Analytics**: Generate custom reports and visualizations about your pipelines directly through conversation.
+4. **Streamlined Workflows**: Trigger pipeline runs via natural language requests when you're ready to execute.
+
+You can get a sense of how it works in the following video:
+
+[![ZenML MCP Server Features](../../.gitbook/assets/mcp-video.png)](https://www.loom.com/share/4cac0c90bd424df287ed5700e7680b14?sid=200acd11-2f1b-4953-8577-6fe0c65cad3c)
+
+## Features
+
+The ZenML MCP server provides access to core read functionality from your ZenML server, allowing you to get live information about:
+
+- Users
+- Stacks
+- Pipelines
+- Pipeline runs
+- Pipeline steps
+- Services
+- Stack components
+- Flavors
+- Pipeline run templates
+- Schedules
+- Artifacts (metadata about data artifacts, not the data itself)
+- Service Connectors
+- Step code
+- Step logs (if the step was run on a cloud-based stack)
+
+It also allows you to trigger new pipeline runs through existing run templates.
+
+## Getting Started
+
+For the most up-to-date setup instructions and code, please refer to the [ZenML
+MCP Server GitHub repository](https://github.com/zenml-io/mcp-zenml). We
+recommend using the `uv` package manager to install the dependencies since it's
+the most reliable and fastest setup experience.
+
+The setup process for the ZenML MCP Server is straightforward:‍
+
+### Prerequisites:
+- Access to a ZenML Cloud server
+- [`uv`](https://docs.astral.sh/uv/) installed locally
+- A local clone of the repository
+
+### Configuration:
+
+- Create an MCP config file with your ZenML server details
+- Configure your preferred MCP client (Claude Desktop or Cursor)
+
+For detailed setup instructions, please refer to the [GitHub repository](https://github.com/zenml-io/mcp-zenml).
+
+## Example Usage
+
+Once set up, you can interact with your ZenML infrastructure through natural language. Here are some example prompts you can try:
+
+1. **Pipeline Analysis Report**:
+   ```
+   Can you write me a report (as a markdown artifact) about the 'simple_pipeline' and tell the story of the history of its runs, which were successful etc., and what stacks worked, which didn't, as well as some performance metrics + recommendations?
+   ```
+
+![Pipeline Analysis Report](../../.gitbook/assets/mcp-pipeline-analysis.png)
+
+2. **Comparative Pipeline Analysis**:
+   ```
+   Could you analyze all our ZenML pipelines and create a comparison report (as a markdown artifact) that highlights differences in success rates, average run times, and resource usage? Please include a section on which stacks perform best for each pipeline type.
+   ```
+
+![Comparative Pipeline Analysis](../../.gitbook/assets/mcp-comparative-analysis.png)
+
+3. **Stack Component Analysis**:
+   ```
+   Please generate a comprehensive report or dashboard on our ZenML stack components, showing which ones are most frequently used across our pipelines. Include information about version compatibility issues and performance variations.
+   ```
+
+![Stack Component Analysis](../../.gitbook/assets/mcp_stack_component_analysis.gif)
+
+## Get Involved
+
+We invite you to try the [ZenML MCP Server](https://github.com/zenml-io/mcp-zenml) and share your experiences with us through our [Slack community](https://zenml.io/slack). We're particularly interested in:
+
+- Whether you need additional write actions (creating stacks, registering components, etc.)
+- Examples of how you're using the server in your workflows
+- Suggestions for additional features or improvements
+
+Contributions and pull requests to [the core repository](https://github.com/zenml-io/mcp-zenml) are always welcome!

docs/book/how-to/manage-zenml-server/vscode-extension.md

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+---
+description: Use the ZenML VSCode extension to manage your ZenML server
+---
+
+# Use the ZenML VSCode extension to manage your ZenML server
+
+The ZenML VSCode extension is a tool that allows you to manage your ZenML server
+from within VSCode. It provides features for stack management, pipeline
+visualization, and project management capabilities. You can use it in any IDE
+which allows the installation of extensions from the VSCode Marketplace, which
+means that Cursor also supports this extension.
+
+![ZenML VSCode Extension](../../.gitbook/assets/vscode-extension.gif)
+
+## How to install the ZenML VSCode extension
+
+You can install the ZenML VSCode extension in several ways:
+
+### From the VSCode Marketplace
+
+1. Open VSCode
+2. Navigate to the Extensions view (Ctrl+Shift+X or Cmd+Shift+X on macOS)
+3. Search for "ZenML"
+4. Click "Install"
+
+### From the Command Line
+
+```bash
+code --install-extension zenml.zenml-vscode
+```
+
+## Features
+
+The ZenML VSCode extension offers several powerful features:
+
+- **Project Management**: Create, manage, and navigate ZenML projects
+- **Stack Visualization**: View and manage your ZenML stacks and components
+- **DAG Visualization**: Visualize your pipeline DAGs for better understanding
+- **Pipeline Run Management**: Monitor and manage your pipeline runs
+- **Stack Registration**: Register new stacks directly from VSCode
+
+## Version Compatibility
+
+The ZenML VSCode extension has different versions that are compatible with specific ZenML library versions. For the best experience, use an extension version that matches your ZenML library.
+
+For a detailed compatibility table, refer to the [ZenML VSCode extension repository](https://github.com/zenml-io/vscode-zenml/blob/develop/VERSIONS.md).
+
+### Installing a Specific Version
+
+If you need to work with an older ZenML version:
+
+#### Using VS Code UI:
+1. Go to the Extensions view (Ctrl+Shift+X)
+2. Search for "ZenML"
+3. Click the dropdown next to the Install button
+4. Select "Install Another Version..."
+5. Choose the version that matches your ZenML library version
+
+#### Using Command Line:
+```bash
+# Example for installing version 0.0.11
+code --install-extension [email protected]
+```
+
+For the best experience, we recommend using the latest version of both the ZenML library and the extension:
+
+```bash
+pip install -U zenml
+```
+
+## Using the Extension
+
+After installation:
+
+1. **Connect to your ZenML server**: Use the ZenML sidebar in VSCode to connect to your ZenML server
+2. **Explore your projects**: Browse through your existing projects or create new ones
+3. **Visualize pipelines**: View DAGs of your pipelines to understand their structure
+4. **Manage stack components**: Visualize and configure stack components
+5. **Monitor runs**: Track the status and details of your pipeline runs
+
+## Troubleshooting
+
+If you encounter issues with the extension:
+
+- Ensure your ZenML library and extension versions are compatible
+- Check your server connection settings
+- Verify that your authentication credentials are correct
+- Try restarting VSCode
+
+For more help, visit the [ZenML GitHub
+repository](https://github.com/zenml-io/vscode-zenml) or send us a message on
+our [Slack community](https://zenml.io/slack).
+
+

docs/book/toc.md

@@ -27,6 +27,8 @@
   * [Upgrade your ZenML server](how-to/manage-zenml-server/upgrade-zenml-server.md)
   * [Best practices for upgrading ZenML](how-to/manage-zenml-server/best-practices-upgrading-zenml.md)
   * [Using ZenML server in production](how-to/manage-zenml-server/using-zenml-server-in-prod.md)
+  * [Use the ZenML VSCode extension](how-to/manage-zenml-server/vscode-extension.md)
+  * [Chat with your ZenML server](how-to/manage-zenml-server/mcp-chat-with-server.md)
   * [Troubleshoot your ZenML server](how-to/manage-zenml-server/troubleshoot-your-deployed-server.md)
   * [Migration guide](how-to/manage-zenml-server/migration-guide/migration-guide.md)
     * [Migration guide 0.13.2 → 0.20.0](how-to/manage-zenml-server/migration-guide/migration-zero-twenty.md)