GrowingIO MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect GrowingIO through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to GrowingIO "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in GrowingIO?"
)
print(result.data)
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 GrowingIO MCP Server
Empower your AI agent to orchestrate your product analytics and user behavioral data with GrowingIO, the premier analytical platform in China. By connecting GrowingIO to your agent, you transform complex event tracking, user segmentation, and metric analysis into a natural conversation. Your agent can instantly list tracked events, retrieve detailed user segment metadata, monitor conversion funnels, and execute quantitative metric queries without you ever needing to navigate the comprehensive GrowingIO web interface. Whether you are conducting a product health audit or monitoring real-time campaign performance, your agent acts as a real-time data analyst assistant, keeping your product data accurate and your growth moving.
Pydantic AI validates every GrowingIO tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
What you can do
- Event Orchestration — List and retrieve detailed metadata for all tracked behavioral events in your project.
- User Segmentation — Browse and monitor user segments to identify high-value cohorts and behavioral patterns.
- Metric Querying — Execute quantitative queries to retrieve specific performance metrics via natural language.
- Funnel Auditing — Retrieve detailed configuration and data for conversion funnels to identify drop-off points.
- Campaign Insights — Browse tracked advertising campaigns and identify successful growth drivers.
The GrowingIO MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI 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 GrowingIO to Pydantic AI via MCP
Follow these steps to integrate the GrowingIO MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
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 GrowingIO with type-safe schemas
Why Use Pydantic AI with the GrowingIO MCP Server
Pydantic AI provides unique advantages when paired with GrowingIO through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your GrowingIO integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your GrowingIO connection logic from agent behavior for testable, maintainable code
GrowingIO + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the GrowingIO MCP Server delivers measurable value.
Type-safe data pipelines: query GrowingIO with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple GrowingIO tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query GrowingIO and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock GrowingIO responses and write comprehensive agent tests
GrowingIO MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect GrowingIO to Pydantic AI via MCP:
get_event
Get event details
get_funnel
Get conversion funnel details
get_metrics
Query project metrics
get_project_info
Get project metadata
get_segment_users
Get users in a segment
list_ads
List advertising campaigns
list_events
List project events
list_log_sources
). List data log sources
list_segments
List user segments
list_variables
List tracked variables
Example Prompts for GrowingIO in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with GrowingIO immediately.
"List all user segments in GrowingIO."
"Show me the conversion funnel for 'Checkout Flow'."
"Query the DAU for the last 7 days."
Troubleshooting GrowingIO MCP Server with Pydantic AI
Common issues when connecting GrowingIO to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiGrowingIO + Pydantic AI FAQ
Common questions about integrating GrowingIO MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
Connect GrowingIO 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 GrowingIO to Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.