How to Use the Easelly MCP in LlamaIndex

Q: How do I index my Easelly templates into a LlamaIndex vector store?

Use generatejson to fetch the layout structures, then load them into your index using standard document parsers. This makes your visual templates searchable via semantic queries.

Q: Can LlamaIndex agents automatically edit an Easelly infographic?

Yes, your agent can use updateinfographic to modify text blocks or chart values based on data retrieved from your documents. The agent maps the query results directly to the layout JSON before sending the update.

Q: How do I set up the Easelly MCP Server with LlamaIndex?

Install llama-index-tools-mcp and initialize the BasicMCPClient with your Vinkius URL. Convert the tools using McpToolSpec and pass them to your FunctionAgent.

Q: Does this integration support filtering tools?

Yes, you can use the allowedtools filter during initialization to restrict your agent to specific actions. This prevents the agent from calling resource-heavy rendering tools when only metadata is needed.

Index your Easelly templates and visual assets directly into LlamaIndex for semantic search via this MCP server.

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Connect Easelly MCP to LlamaIndex

Create your Vinkius account to connect Easelly 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 Easelly with LlamaIndex

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Index visual layouts into your LlamaIndex RAG pipeline

The `generate_json` tool extracts the full structural schema of your Easelly infographics so LlamaIndex can index them into your vector database. This lets your LlamaIndex agent perform semantic queries over your visual library, finding specific charts or text blocks based on meaning rather than filename. By converting Easelly visual layouts into searchable LlamaIndex text chunks, you bridge the gap between static graphics and dynamic RAG applications. Your LlamaIndex agent can query past designs, pull the exact layout structure, and write grounded answers based on actual design data.

Retrieve and update templates using an MCP Server

The `list_infographics` tool lists all available designs in your Easelly account, allowing LlamaIndex to build a live catalog of your visual assets. Your LlamaIndex agent searches this catalog to find the correct template before starting an automated update cycle. Once the LlamaIndex agent identifies the correct Easelly template, it uses the vector index to match your incoming data queries with the corresponding layout blocks. This ensures your automated updates always target the right visual elements without manual template mapping.

Generate contextual images based on vector queries

The `generate_image` tool renders your data-driven Easelly layouts into static PNG files that your LlamaIndex agent can serve to users. The LlamaIndex agent triggers this rendering process only after validating that the layout matches the retrieved context from your vector store. This creates a closed-loop LlamaIndex system where user queries fetch relevant data, update the Easelly template, and render a fresh image. You get dynamic, context-aware visual assets that update themselves based on the latest documents in your LlamaIndex index.

Setup guide

Set up Easelly 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 Easelly 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 Easelly tools.",
)
response = await agent.run("List recent Easelly data")

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Common questions about Easelly MCP in LlamaIndex

Use `generate_json` to fetch the layout structures, then load them into your index using standard document parsers. This makes your visual templates searchable via semantic queries.

Yes, your agent can use `update_infographic` to modify text blocks or chart values based on data retrieved from your documents. The agent maps the query results directly to the layout JSON before sending the update.

Install `llama-index-tools-mcp` and initialize the `BasicMCPClient` with your Vinkius URL. Convert the tools using `McpToolSpec` and pass them to your `FunctionAgent`.

Yes, you can use the `allowed_tools` filter during initialization to restrict your agent to specific actions. This prevents the agent from calling resource-heavy rendering tools when only metadata is needed.

Every template metadata and layout configuration is processed in an isolated sandbox environment. Vinkius ensures that no indexing data or layout configurations are cached or exposed to external networks, maintaining strict MCP data boundaries.

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