Add docs for MCP Server and VSCode Extension (#3475) (#3477)

* add mcp docs

* Add vscode extension docs

* Update TOC

* Optimised images with calibre/image-actions

* Fix broken URL

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Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
(cherry picked from commit 4f406c1)

Author: strickvl

docs/book/.gitbook/assets/mcp-comparative-analysis.png

@@ -86,7 +86,7 @@ Walk us through how to reproduce the same error you had step-by-step, whenever p
 As a general rule of thumb, always attach relevant log outputs and the full
 error traceback to help us understand what happened under the hood. If the full
 error traceback does not fit into a text message, attach a file or use a service
-like [Pastebin](https://pastebin.com/) or [Github's Gist](https://gist.github.com/).
+like Pastebin or [Github's Gist](https://gist.github.com/).
 
 Along with the error traceback, we recommend to always share the output of the following commands:
 

docs/book/.gitbook/assets/mcp-pipeline-analysis.png

@@ -0,0 +1,108 @@
+---
+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/.gitbook/assets/mcp-video.png

@@ -0,0 +1,94 @@
+---
+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/.gitbook/assets/mcp-zenml.png

@@ -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)