This is a submission for the Storyblok Challenge

What I Built

I built a modular, extensible Storyblok MCP Server that allows seamless integration between AI assistants and the full capabilities of the Storyblok Management API.

This Storyblok MCP (Model Context Protocol) Server enables your AI assistants to directly access and manage every aspect of a Storyblok spaces, stories, components, assets, workflows, and more...

This project aims to remove the barrier between conversational interfaces and content operations, enabling developers, marketers, and content creators to interact with Storyblok hands-free.

I’ve implemented 155+ MCP tools, covering nearly all endpoints in the Storyblok Management API.

With over 155+ mcp tools Your AI assistant can now:

• Create - Create new stories, components, assets, datasources, tags, releases, workflows, and more.

• Read - Read all your stories, components, assets, datasources, tags, releases, workflows, and more.

• Update - Update existing/new stories, components, assets, datasources, tags, releases, workflows, and more.

• Delete - Delete specific/all your stories, components, assets, datasources, tags, releases, workflows, and more.

🔧 No manual API definitions needed

🎯 All operations are abstracted into MCP tools

Why This Matters? 🤔

• 👨‍💻 For Developers: Instantly expose all Storyblok operations as AI tools, no more writing repetitive API code.

• ⚡ For Teams: Empower marketers, editors, and non-technical users to manage content with natural language.

• 💪 For AI Builders: Use this as a plug-and-play backend for any LLM or agent that supports tool calling.

Demo

Code Repository:

Checkout the repo for detailed instructions and to see all available tools. You can view the full list of tools directly in the Tools Section of the README. (⭐ to show your support 🙂)

Kiran1689 / storyblok-mcp-server

A modular, extensible MCP Server for managing Storyblok spaces, stories, components, assets, workflows, and more via the Model Context Protocol (MCP).

Storyblok MCP Server 🚀

The Storyblok MCP (Model Context Protocol) server enables your AI assistants to directly access and manage your Storyblok spaces, stories, components, assets, workflows, and more.

What Does It Do?

With the Storyblok MCP Server, your AI assistant can:

• Create - Create new stories, components, assets, datasources, tags, releases, workflows, and more.
• Read - Read all your stories, components, assets, datasources, tags, releases, workflows, and more.
• Update - Update existing/new stories, components, assets, datasources, tags, releases, workflows, and more.
• Delete - Delete specific/all your stories, components, assets, datasources, tags, releases, workflows, and more.

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🚀 Features

• Full Storyblok Management: CRUD for stories, components, assets, datasources, tags, releases, workflows, and more.(Covered everything)
• Modular Tooling: Each Storyblok resource is managed by its own tool module for easy extension and maintenance.
• Meta Tool: Discover all available tools and their descriptions at runtime.
• Async & Fast…

View on GitHub

Demo Video

Watch the Full Demo Video here👇

I have only covered stories tools in the video for demo purpose. Feel free to use it however you want after connecting to MCP server.

⚡️ Quickstart

• Clone the repo

git clone https://github.com/Kiran1689/storyblok-mcp-server
cd storyblok-mcp-server

• Install dependencies

pip install -r requirements.txt

• Configure your environment Copy .env.example to .env and fill in your Storyblok credentials:

SORYBLOK_SPACE_ID=your_space_id
STORYBLOK_MANAGEMENT_TOKEN=your_management_token
STORYBLOK_DEFAULT_PUBLIC_TOKEN=your_public_token

• MCP Client Configuration To use this server with Claude or any MCP client, copy the following into your claude_desktop_config.json:

{
    "mcpServers": {
        "storyblok": {
            "command": "uv",
            "args": [
                "run",
                "--with",
                "mcp",
                "mcp",
                "run",
                "C:\\path\\to\\storyblok-mcp-server\\server.py"
            ],
            "env": {
                "STORYBLOK_SPACE_ID": "your_space_id",
                "STORYBLOK_MANAGEMENT_TOKEN": "your_management_token",
                "STORYBLOK_DEFAULT_PUBLIC_TOKEN": "your_public_token"
            }
        }
    }
}

• Paste this config into your Claude or MCP client to connect instantly.

💡 NOTE: Make sure you have installed uv on your system

Restart your Claude Desktop and check the tools. If connected, you’ll see the total number of tools available.

• Run and Test Locally You can also run and test the server locally using MCP Inspector:

mcp run server.py

Tech Stack

I used:

• Python (HTTPX, AsyncIO)
• MCP Python SDK
• FastMCP (for defining AI tools)
• Storyblok Management API

How I Used Storyblok

Storyblok's Management API is the backbone of this entire project. Every endpoint is mapped to an AI tool, so you never have to deal with tokens, URLs, or payloads manually.

The Resources I covered from Storyblok's Management API 👇

AI Integration

This project is built to be AI-native. Any LLM or AI agent that supports tool calling can use this server to call the tools automatically.

Each MCP tool is designed so an AI agent can:

• Understand its purpose via tool descriptions.

• Call it with minimal inputs.

I tested the server with Claude Desktop and the Claude Sonnet 4 model, the results were accurate and fast. You can see this in the demo video or try it yourself.

Real-World Use Cases

• Automated Content Workflows:
  Let your AI assistant publish, update, or archive content on a schedule or in response to business events.

• Conversational Content Management:
  Non-technical users can ask for reports, create new stories, or update assets—all via chat.

• Bulk Operations:
  Instantly update, delete, or migrate hundreds of stories or assets with a single command.

Learnings and Takeaways

To be honest, I’ve always wanted to build a custom MCP server that’s genuinely useful for others. This challenge gave me the push to do it!

• Learned how to structure AI tools using FastMCP for real-world use.
• Discovered MCP Inspector for debugging and testing MCP servers. It was super helpful!
• Overcame the challenge of managing 150+ endpoints by building a uniform abstraction layer.

Thanks for this opportunity🫶

If you have questions or want to contribute, check out the repo or open an issue.

Kiran NaragundFollow

Tech Writer and Moderator @DEV ✦ Full-Stack Developer ✦ Mentor @Exercism ✦ Open-Source Contributor ✦ Email for Collabs :)