Storyblok MCP Server for LangChain 9 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Storyblok through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"storyblok": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Storyblok, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Storyblok MCP Server
Integrate the powerful headless CMS capabilities of Storyblok directly into your conversational AI. Empower your content teams and developers to organically draft narratives, parse complex asset repositories, and orchestrate page component definitions without relying entirely on the visual editor. Bind your AI local context directly to your Storyblok environment securely, enabling programmatic schema generation and continuous iteration utilizing a streamlined conversational interface designed to accelerate creative velocity.
LangChain's ecosystem of 500+ components combines seamlessly with Storyblok through native MCP adapters. Connect 9 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
What you can do
- Space & Content Discovery — Instantly list active enterprise environments utilizing
list_spacesand fetch broad overarching overviews referencing stories vialist_stories. - Content Construction — Swiftly produce or update textual assets creating schemas directly from prompts invoking
create_content_storyandupdate_content_storysystematically. - Asset & Structure Exploration — Analyze media repositories via
list_assetsand precisely inspect available schema blueprints callinglist_componentsto standardize development. - Risk Management — Exercise safe administrative control over local projects, evaluating internal authorized operators implementing modifications using
list_space_users.
The Storyblok MCP Server exposes 9 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Storyblok to LangChain via MCP
Follow these steps to integrate the Storyblok MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 9 tools from Storyblok via MCP
Why Use LangChain with the Storyblok MCP Server
LangChain provides unique advantages when paired with Storyblok through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine Storyblok MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Storyblok queries for multi-turn workflows
Storyblok + LangChain Use Cases
Practical scenarios where LangChain combined with the Storyblok MCP Server delivers measurable value.
RAG with live data: combine Storyblok tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Storyblok, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Storyblok tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Storyblok tool call, measure latency, and optimize your agent's performance
Storyblok MCP Tools for LangChain (9)
These 9 tools become available when you connect Storyblok to LangChain via MCP:
create_content_story
Provide a name, slug, and content JSON. Creates a new story in a Storyblok space
delete_content_story
This action is irreversible. Permanently deletes a Storyblok story
get_story_details
Retrieves details for a specific content story
list_assets
Lists media assets in a Storyblok space
list_components
Lists available content components
list_space_users
Lists all users with access to a specific space
list_spaces
Lists all accessible Storyblok spaces
list_stories
Requires a space ID. Lists content stories within a specific space
update_content_story
Requires space and story IDs. Updates fields of an existing Storyblok story
Example Prompts for Storyblok in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Storyblok immediately.
"List the recent articles from my Storyblok space and detail their structural components."
"List the structure blueprints by calling list_components and then formulate a new JSON to create a blog story."
"List all multimedia assets in my Storyblok space and display their URLs."
Troubleshooting Storyblok MCP Server with LangChain
Common issues when connecting Storyblok to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersStoryblok + LangChain FAQ
Common questions about integrating Storyblok MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Storyblok with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Storyblok to LangChain
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.