Tinybird Data Platform MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Tinybird Data Platform through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"tinybird-data-platform": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Tinybird Data Platform, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Tinybird Data Platform through native MCP adapters. Connect 10 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the Tinybird Data Platform MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 10 tools from Tinybird Data Platform via MCP
Why Use LangChain with the Tinybird Data Platform MCP Server
LangChain provides unique advantages when paired with Tinybird Data Platform through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine Tinybird Data Platform MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Tinybird Data Platform queries for multi-turn workflows
Tinybird Data Platform + LangChain Use Cases
Practical scenarios where LangChain combined with the Tinybird Data Platform MCP Server delivers measurable value.
RAG with live data: combine Tinybird Data Platform tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Tinybird Data Platform, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Tinybird Data Platform tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Tinybird Data Platform tool call, measure latency, and optimize your agent's performance
Tinybird Data Platform MCP Tools for LangChain (10)
These 10 tools become available when you connect Tinybird Data Platform to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Tinybird Data Platform to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersTinybird Data Platform + LangChain FAQ
Common questions about integrating Tinybird Data Platform MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.