Tinybird Data Platform MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Tinybird Data Platform as an MCP tool provider through the 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 Tinybird Data Platform. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Tinybird Data Platform?"
)
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 Tinybird Data Platform MCP Server
Connect your AI agent to Tinybird, the real-time data platform for developers. This integration allows you to oversee your analytical infrastructure, manage ingestion storage (Data Sources), and interact with transformation logic (Pipes) through natural conversation.
LlamaIndex agents combine Tinybird Data Platform tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the 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
- Infrastructure Oversight — List and inspect all your Data Sources and Pipes in real-time
- Transformation Analysis — Retrieve SQL logic and nodes for any Pipe to understand how data is being processed
- Live Querying — Execute published Pipes or run arbitrary SQL queries (ClickHouse dialect) to explore your data directly via the agent
- Operational Metrics — Check ingestion stats, row counts, and storage sizes for your analytical units
- Access Control — List and audit authentication tokens and workspace configurations
The Tinybird Data Platform MCP Server exposes 10 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 Tinybird Data Platform to LlamaIndex via MCP
Follow these steps to integrate the Tinybird Data Platform 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 10 tools from Tinybird Data Platform
Why Use LlamaIndex with the Tinybird Data Platform MCP Server
LlamaIndex provides unique advantages when paired with Tinybird Data Platform through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Tinybird Data Platform tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Tinybird Data Platform tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Tinybird Data Platform, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Tinybird Data Platform tools were called, what data was returned, and how it influenced the final answer
Tinybird Data Platform + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Tinybird Data Platform MCP Server delivers measurable value.
Hybrid search: combine Tinybird Data Platform real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Tinybird Data Platform 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 Tinybird Data Platform for fresh data
Analytical workflows: chain Tinybird Data Platform queries with LlamaIndex's data connectors to build multi-source analytical reports
Tinybird Data Platform MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Tinybird Data Platform to LlamaIndex via MCP:
execute_sql_query
Execute an arbitrary SQL query against the Tinybird workspace
get_datasource_details
Get comprehensive information for a specific Data Source
get_datasource_stats
Retrieve ingestion and usage statistics for a Data Source
get_pipe_details
Get detailed information for a specific Pipe
list_auth_tokens
Retrieve a list of all authentication tokens in the workspace
list_datasources
Retrieve a list of all Data Sources in the current workspace
list_pipe_nodes
List all SQL nodes within a specific Pipe
list_pipes
Retrieve a list of all Pipes in the current workspace
list_workspaces
Retrieve a list of available workspaces
query_pipe_data
Execute a Pipe and retrieve the results as JSON
Example Prompts for Tinybird Data Platform in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Tinybird Data Platform immediately.
"List all data sources in my Tinybird workspace."
"Run the pipe 'monthly_revenue_summary' with limit 5."
Troubleshooting Tinybird Data Platform MCP Server with LlamaIndex
Common issues when connecting Tinybird Data Platform to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpTinybird Data Platform + LlamaIndex FAQ
Common questions about integrating Tinybird Data Platform 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 Tinybird Data Platform 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 Tinybird Data Platform to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.