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.
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 🙂)
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.
🚀 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.
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 👇
Resource
Description
Access Tokens
Manage access tokens for Storyblok API
Activities
Manage or retrieve activity logs
Approvals
Manage approval workflows
Assets
Manage assets (upload, update, delete, list)
Assets Folder
Manage asset folders
Branch Deployments
Manage branch deployments
Collaborators
Manage collaborators in a space
Components
Manage Storyblok components (CRUD, schema, etc.)
Components Folder
Manage folders for components
Datasource Entries
Manage entries in data sources
Data Sources
Manage data sources (CRUD, entries)
Discussions
Manage discussions and comments
Extensions
Manage Storyblok extensions
Field Plugins
Manage custom field plugins
Internal Tags
Manage internal tags for assets/stories
Meta
Meta tool: discover all available tools
Ping
Health check and server status
Pipelines
Manage pipelines for content delivery
Presets
Manage field presets for components
Releases
Manage releases (create, update, publish)
Scheduling Stories
Schedule stories for publishing
Space
Manage Storyblok space settings and info
Space Roles
Manage roles and permissions in a space
Stories
Manage stories (CRUD, bulk ops, validation)
Tags
Manage tags (CRUD, bulk association)
Tasks
Manage tasks (CRUD, webhooks, automation)
Webhooks
Manage webhooks (CRUD, trigger)
Workflows
Manage workflows and workflow stages
Workflow Stage
Manage individual workflow stages
Workflow Stage Changes
Track and manage workflow stage changes
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.
This is really next level - 155+ tools abstracted for AI is super impressive. Have you tried this with agents outside of Claude or hooked it into custom chat interfaces yet?
Amazing work! Super impressed by how you integrated AI with Storyblock so seamlessly. 155+ tools is no joke huge impact for both devs and content teams.
Awesome work, appreciate the share. You should check out run-time API discovery as a potentially more lightweight solution. check out the whitepaper here.
Loved It!!