How to Use the Hygraph (Headless CMS) MCP in LlamaIndex

Q: How do I build a RAG pipeline using Hygraph (Headless CMS) and LlamaIndex?

You use executegraphqlquery to pull your content records directly into LlamaIndex. From there, the framework chunks the text and loads it into your vector store for semantic search.

Q: Can LlamaIndex handle localized content from Hygraph (Headless CMS)?

Yes, by calling listprojectlocales first, your LlamaIndex agent identifies all active language spaces. It then queries and indexes the correct localized fields for your multi-lingual search applications.

Q: How does LlamaIndex verify schema structures in Hygraph (Headless CMS) before indexing?

The framework uses listschemaintrospection via this MCP Server to discover your models automatically. This allows LlamaIndex to map your content fields to vector metadata without manual schema definitions.

Q: How do I register this MCP Server with LlamaIndex?

Install the llama-index-tools-mcp package and initialize the client with your Vinkius URL. This exposes all ten Hygraph tools to your agent as standard LlamaIndex tools.

Index your live Hygraph (Headless CMS) content and schemas directly into LlamaIndex vector stores for hallucination-free RAG.

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Connect Hygraph (Headless CMS) MCP to LlamaIndex

Create your Vinkius account to connect Hygraph (Headless CMS) to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Hygraph (Headless CMS) with LlamaIndex

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Index Hygraph Content for LlamaIndex RAG

The `execute_graphql_query` tool fetches your live CMS documents via this MCP Server so LlamaIndex can parse, chunk, and index them into your vector database. This keeps your search index grounded in real-time content rather than outdated static files. Your retrieval-augmented generation pipelines query this fresh index to answer user questions. Because the agent pulls directly from your live API, it eliminates hallucinations regarding your published product specs or articles.

Inspect Model Fields before LlamaIndex Queries

The `get_model_fields` tool provides your LlamaIndex query engine with the exact schema definitions of your Hygraph models. This prevents indexing errors by letting the engine map metadata fields correctly before ingestion. You can filter content based on specific localized fields by querying `list_project_locales` first. This ensures your vector store indexes the correct translation strings for multi-region search pipelines.

Manage Live CMS Data through LlamaIndex Agents

The `update_cms_document` tool allows your knowledge-augmented agent to modify content drafts using the MCP standard. The agent updates your headless database directly without leaving the RAG loop. If you need to clean up outdated or redundant test documents, the agent triggers `wipe_cms_document` to remove them. This keeps your production environment clean and prevents stale data from polluting your search index.

Setup guide

Set up Hygraph (Headless CMS) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Hygraph (Headless CMS) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Hygraph (Headless CMS) tools.",
)
response = await agent.run("List recent Hygraph (Headless CMS) data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Hygraph. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Hygraph (Headless CMS) MCP in LlamaIndex

You use `execute_graphql_query` to pull your content records directly into LlamaIndex. From there, the framework chunks the text and loads it into your vector store for semantic search.

Yes, by calling `list_project_locales` first, your LlamaIndex agent identifies all active language spaces. It then queries and indexes the correct localized fields for your multi-lingual search applications.

The framework uses `list_schema_introspection` via this MCP Server to discover your models automatically. This allows LlamaIndex to map your content fields to vector metadata without manual schema definitions.

Install the `llama-index-tools-mcp` package and initialize the client with your Vinkius URL. This exposes all ten Hygraph tools to your agent as standard LlamaIndex tools.

Yes, your schema definitions and GraphQL queries are processed strictly in memory within ephemeral, isolated environments. Vinkius secures your Permanent Auth Tokens, ensuring no credentials or database structures are stored or exposed to external networks.

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