Miro MCP Server for LlamaIndex 14 tools — connect in under 2 minutes
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 MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 14 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 take full control of your visual collaboration through natural conversation.
LlamaIndex agents combine Miro tool responses with indexed documents for comprehensive, grounded answers. Connect 14 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, create, update and inspect boards with their descriptions, owners and permissions
- Item Operations — Browse all widgets on a board (sticky notes, cards, shapes, texts, connectors, images) with their content and positions
- Content Creation — Create sticky notes and cards programmatically with custom content and canvas positions
- Member Management — List board members and add new users with specific roles (owner, admin, editor, commenter, viewer)
- Comments — Read and add comments on boards for async collaboration feedback
The Miro MCP Server exposes 14 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.
How to Connect Miro to LlamaIndex via MCP
Follow these steps to integrate the Miro MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 14 tools from Miro
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
Miro MCP Tools for LlamaIndex (14)
These 14 tools become available when you connect Miro to LlamaIndex via MCP:
add_board_member
Requires the board ID and user ID. Optionally set the role (owner, admin, editor, commenter, viewer). The user must already have a Miro account. Add a member to a Miro board
create_board
Requires the board name and optionally a description. Returns the created board with its ID, view link and edit link. Create a new Miro board
create_card
Requires the board ID and card title. Optionally set a description and x,y position. Cards are structured content widgets with title and description fields. Create a card widget on a Miro board
create_comment
Requires the board ID and comment content. Optionally reply to an existing comment by providing its ID as parent_id. Add a comment to a Miro board
create_sticky_note
Requires the board ID and the sticky note content (text). Optionally set the x,y position on the canvas. Returns the created sticky note with its ID and position. Create a sticky note on a Miro board
delete_board_item
Provide the board ID and item ID. WARNING: this action is irreversible. Delete an item from a Miro board
get_board
Provide the board ID (found in the board URL or from list_boards). Get details for a specific Miro board
get_board_item
Provide the board ID and item ID. Get details for a specific item on a Miro board
get_user_context
Returns user ID, name, email, avatar and account type. Use this to verify your access token is working correctly and to see which user identity the API calls will appear as. Get the authenticated Miro user context
list_board_items
) placed on a Miro board. Each item includes its type, ID, content, position, rotation, size and style. Optionally filter by item type (sticky_note, card, shape, text, connector, image, embed, frame, document, mind_map) and set a limit. List items (widgets) on a Miro board
list_board_members
Each member shows their user ID, name, email, role (owner, admin, editor, commenter, viewer) and permission level. Optionally set a limit. List members of a Miro board
list_boards
Each board includes its ID, name, description, creation date, owner and permissions. Optionally set a limit on the number of results. Use this to discover boards before accessing their content. List Miro boards accessible by the user
list_comments
Each comment includes its ID, content text, author info, creation date and parent comment ID (for replies). Optionally set a limit. List comments on a Miro board
update_board
Provide the board ID and the new name and/or description. Only provided fields will be updated. Update a Miro board name or description
Example Prompts for Miro in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Miro immediately.
"Show me all the sticky notes on my Sprint Planning board."
"Create a new board called 'Q2 OKRs' with a description 'Quarterly objectives and key results'."
"Add a sticky note to my board saying 'Meeting notes: Discussed API versioning strategy' at position x:100, y:200."
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.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Miro with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Miro to LlamaIndex
Get your token, paste the configuration, and start using 14 tools in under 2 minutes. No API key management needed.