Webiny CMS MCP Server for LlamaIndex 9 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Webiny CMS as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Webiny CMS. "
"You have 9 tools available."
),
)
response = await agent.run(
"What tools are available in Webiny CMS?"
)
print(response)
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.
LlamaIndex agents combine Webiny CMS tool responses with indexed documents for comprehensive, grounded answers. Connect 9 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Webiny CMS MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 9 tools from Webiny CMS
Why Use LlamaIndex with the Webiny CMS MCP Server
LlamaIndex provides unique advantages when paired with Webiny CMS through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Webiny CMS tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Webiny CMS tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Webiny CMS, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Webiny CMS tools were called, what data was returned, and how it influenced the final answer
Webiny CMS + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Webiny CMS MCP Server delivers measurable value.
Hybrid search: combine Webiny CMS real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Webiny CMS to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Webiny CMS for fresh data
Analytical workflows: chain Webiny CMS queries with LlamaIndex's data connectors to build multi-source analytical reports
Webiny CMS MCP Tools for LlamaIndex (9)
These 9 tools become available when you connect Webiny CMS to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Webiny CMS to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpWebiny CMS + LlamaIndex FAQ
Common questions about integrating Webiny CMS MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.