How to Use the Agility CMS MCP in LlamaIndex

Q: Can I filter which Agility CMS data LlamaIndex indexes?

Yes. You can use the allowedtools filter to restrict the agent to specific CMS tools. This keeps your index focused on the data you care about.

Index your Agility CMS content into LlamaIndex vector stores for ground-truth RAG applications.

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Connect Agility CMS MCP to LlamaIndex

Create your Vinkius account to connect Agility 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 Agility CMS with LlamaIndex

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RAG-ready indexing for LlamaIndex

Turn your CMS content into a searchable knowledge base. By running `list_content_items` through your ingestion pipeline, you populate vector stores with actual, live data from your site. Stop relying on static dumps. Your LlamaIndex agents will query the latest site state every time they need to answer a question about your products or articles.

Deep content retrieval for agents

Pull specific modules and layouts using `get_page_layout`. This gives your agent the exact structure of your pages so it understands the context of the data it just searched. It makes your RAG outputs more accurate. Instead of just grabbing text, your agent sees how that text fits into your page architecture.

Searchable reference lists in LlamaIndex

Expose your reference lists to semantic search using `search_content_reference`. Your agent can now find related items by concept rather than just matching exact string IDs. This turns your CMS into a live database. You define the search parameters, and the server provides the relevant content items for your index.

Setup guide

Set up Agility 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 Agility 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 Agility CMS tools.",
)
response = await agent.run("List recent Agility 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 Agility CMS. 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 Agility CMS MCP in LlamaIndex

It integrates directly. You fetch the content, chunk it, and embed it into your LlamaIndex store using standard ingestion patterns.

It feeds high-fidelity, structured data into your index. Your agent gets cleaner context, which leads to fewer hallucinations when answering user questions.

Yes. You can use the allowed_tools filter to restrict the agent to specific CMS tools. This keeps your index focused on the data you care about.

You can trigger incremental syncs to refresh your vector store. It ensures your knowledge base stays current as your content team makes edits.

Data access is restricted to the specific API tokens you provide. The server acts as a sandbox, ensuring your private content items are only read by your authorized LlamaIndex agents.

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