Webiny CMS MCP Server for LangChain 9 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Webiny CMS through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
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({
"webiny-cms": {
"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 Webiny CMS, 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 Webiny CMS MCP Server
Connect your Webiny CMS instance to any AI agent and manage your headless content infrastructure through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Webiny CMS through native MCP adapters. Connect 9 tools via 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
- Content Lifecycle — Create, update, publish, and delete content entries for any model directly from your agent
- Model Discovery — List all entries for specific content models and browse available data structures using introspection
- Advanced GraphQL — Execute raw GraphQL queries or mutations for custom logic and complex nested data operations
- Revision Control — Retrieve specific entry details by ID to inspect metadata and field-level property values
- API Management — Discover available types, fields, and models in your current environment through automated introspection
- Global Config — Verify high-level tenant settings and configurations to ensure your CMS environment is healthy
- Multi-Locale Support — Seamlessly manage content across different language locales (e.g., en-US, pt-BR)
The Webiny CMS 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 Webiny CMS to LangChain via MCP
Follow these steps to integrate the Webiny CMS 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 Webiny CMS via MCP
Why Use LangChain with the Webiny CMS MCP Server
LangChain provides unique advantages when paired with Webiny CMS through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Webiny CMS 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 Webiny CMS queries for multi-turn workflows
Webiny CMS + LangChain Use Cases
Practical scenarios where LangChain combined with the Webiny CMS MCP Server delivers measurable value.
RAG with live data: combine Webiny CMS tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Webiny CMS, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Webiny CMS tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Webiny CMS tool call, measure latency, and optimize your agent's performance
Webiny CMS MCP Tools for LangChain (9)
These 9 tools become available when you connect Webiny CMS to LangChain via MCP:
create_cms_entry
Provide the singular model name and field data as a JSON object. Creates a new draft entry for a content model
delete_cms_entry
This action is irreversible. Permanently deletes a content entry revision
execute_graphql_query
Specify api_type (manage, read, preview) and locale. Executes a raw GraphQL query or mutation against the Webiny CMS API
get_api_introspection
Retrieves the GraphQL schema introspection for the Webiny instance
get_model_entry_details
ID refers to the specific revision. Retrieves details for a specific content model entry
get_tenant_config
Retrieves global settings for the Webiny tenant
list_model_entries
Provide the model plural name (e.g. "Articles"). Specify api_type (manage for drafts, read for live). Lists all entries for a specific content model in Webiny
publish_cms_entry
Provide the specific revision ID. Publishes a draft entry, making it available via the "read" API
update_cms_entry
Provide the entry ID and a JSON object containing the field updates. Updates fields of an existing content entry revision
Example Prompts for Webiny CMS in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Webiny CMS immediately.
"List all entries for the 'BlogPosts' model in en-US."
"Create a new 'Author' entry: { 'name': 'John Doe', 'bio': 'Tech Writer' } in en-US."
"Publish the entry with ID 'post-123' for model 'Article'."
Troubleshooting Webiny CMS MCP Server with LangChain
Common issues when connecting Webiny CMS to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersWebiny CMS + LangChain FAQ
Common questions about integrating Webiny CMS 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 Webiny CMS 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 Webiny CMS to LangChain
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.