How to Use the Miro MCP in LlamaIndex

Q: How does LlamaIndex ingest data from a Miro board?

Your agent calls listboarditems to pull the text from every sticky note on the canvas. LlamaIndex then chunks this raw text and stores it in your vector database for quick semantic retrieval.

Q: Can I query Miro canvas layouts using LlamaIndex RAG?

Yes, by combining getboarddetails with your vector store index, your agent can answer questions about who is collaborating on a board and what topics are currently being mapped out.

Q: How do I filter which Miro boards get indexed in LlamaIndex?

You use the listboards tool to fetch your active projects, then apply a filter in your LlamaIndex ingestion pipeline. This ensures you only index relevant project spaces instead of cluttering your vector store.

Index your Miro board content into LlamaIndex vector stores using this MCP Server to query your team's visual brainstorms.

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Connect Miro MCP to LlamaIndex

Create your Vinkius account to connect Miro 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 Miro with LlamaIndex

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Index Miro board items with LlamaIndex

The `list_board_items` tool extracts all text, ideas, and notes directly from your canvas so LlamaIndex can parse them. Your agent converts this unstructured visual data into searchable vector embeddings. Instead of scrolling through massive boards to find an old idea, you query your index. The framework matches your natural language question with the exact coordinates and text of the relevant sticky notes.

Build RAG pipelines from board details

The `get_board_details` tool retrieves metadata and structural information about your workspaces to ground your agent's answers. LlamaIndex uses this raw context to prevent hallucinations when answering questions about project scopes. This creates a unified knowledge base where live canvas data sits alongside your static documents. Your agent references actual board layouts and member lists to give you accurate, context-rich summaries.

Generate boards grounded in indexed data

The `create_board` tool lets your LlamaIndex agent build new workspaces based on existing documents or past meeting notes. The agent searches your vector index for relevant themes, then structures a fresh canvas to match. You can immediately follow this up with `create_sticky_note` calls to pre-populate the board with curated tasks or ideas. It bridges the gap between your static documentation and your active brainstorming sessions.

Setup guide

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

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Common questions about Miro MCP in LlamaIndex

Your agent calls `list_board_items` to pull the text from every sticky note on the canvas. LlamaIndex then chunks this raw text and stores it in your vector database for quick semantic retrieval.

Yes, by combining `get_board_details` with your vector store index, your agent can answer questions about who is collaborating on a board and what topics are currently being mapped out.

You use the `list_boards` tool to fetch your active projects, then apply a filter in your LlamaIndex ingestion pipeline. This ensures you only index relevant project spaces instead of cluttering your vector store.

Only if you explicitly write it to a persistent vector database. The MCP Server itself is stateless and simply passes the raw canvas data to your LlamaIndex application during execution.

The server reads your board items, sticky notes, and organization details directly from the official API. Vinkius runs this MCP Server inside a zero-trust sandbox, ensuring your private workspace data is never logged or exposed to third parties.

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