Miro MCP Server for LlamaIndexGive LlamaIndex instant access to 8 tools to Create Board, Create Sticky Note, Delete Board, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Miro as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The Miro app connector for LlamaIndex is a standout in the Industry Titans category — giving your AI agent 8 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Miro. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Miro?"
)
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 Miro MCP Server
Connect your Miro account to any AI agent and simplify your visual collaboration and whiteboarding workflows through natural conversation.
LlamaIndex agents combine Miro tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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
- Board Management — List all collaborative boards, retrieve detailed metadata, and create new boards programmatically
- Visual Content Control — List all items on a board (sticky notes, shapes, text) and place new content directly from your agent
- Member Coordination — Query board members and sharing settings to manage access across your organization
- Operational tracking — Monitor board activity and stay on top of your visual projects
- Direct Whiteboarding — Add sticky notes and ideas to a board without leaving your conversation
The Miro MCP Server exposes 8 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.
All 8 Miro tools available for LlamaIndex
When LlamaIndex connects to Miro through Vinkius, your AI agent gets direct access to every tool listed below — spanning whiteboarding, visual-collaboration, brainstorming, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Create a new Miro board
Add a sticky note to a board
Delete a Miro board
Get details for a specific board
List all items on a board
List board collaborators
List Miro boards
List linked organizations
Connect Miro to LlamaIndex via MCP
Follow these steps to wire Miro into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Miro MCP Server
LlamaIndex provides unique advantages when paired with Miro through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Miro tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Miro tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Miro, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Miro tools were called, what data was returned, and how it influenced the final answer
Miro + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Miro MCP Server delivers measurable value.
Hybrid search: combine Miro real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Miro 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 Miro for fresh data
Analytical workflows: chain Miro queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Miro in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Miro immediately.
"List all boards in my Miro account."
"Add a sticky note with 'Implement SSO' to the 'Engineering' board."
"Who has access to the 'Confidential Strategy' board?"
Troubleshooting Miro MCP Server with LlamaIndex
Common issues when connecting Miro to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMiro + LlamaIndex FAQ
Common questions about integrating Miro MCP Server with LlamaIndex.