Where do I find my Tinybird Admin Token?

Log in to your [**Tinybird Dashboard**](https://app.tinybird.co/), select a workspace, and go to the 'Auth Tokens' section. You can use the Admin Token or create a custom one with the necessary scopes.

Which SQL dialect does Tinybird use?

Tinybird is powered by ClickHouse, so it uses the ClickHouse SQL dialect, which includes powerful functions for high-performance analytical processing.

Tinybird Data Platform MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Tinybird Data Platform
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Tinybird Data Platform through the Vinkius — pass the Edge URL in the `mcps` parameter and every Tinybird Data Platform tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Tinybird Data Platform Specialist",
    goal="Help users interact with Tinybird Data Platform effectively",
    backstory=(
        "You are an expert at leveraging Tinybird Data Platform tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Tinybird Data Platform "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, Tinybird Data Platform becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Tinybird Data Platform tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the Tinybird Data Platform MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 10 tools from Tinybird Data Platform

Why Use CrewAI with the Tinybird Data Platform MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Tinybird Data Platform through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Tinybird Data Platform + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Tinybird Data Platform MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Tinybird Data Platform for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Tinybird Data Platform, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Tinybird Data Platform tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Tinybird Data Platform against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Tinybird Data Platform MCP Tools for CrewAI (10)

These 10 tools become available when you connect Tinybird Data Platform to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

Common issues when connecting Tinybird Data Platform to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Tinybird Data Platform + CrewAI FAQ

Common questions about integrating Tinybird Data Platform MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.