Convertlab MCP Server for Pydantic AI 8 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Convertlab 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 Convertlab "
"(8 tools)."
),
)
result = await agent.run(
"What tools are available in Convertlab?"
)
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 Convertlab MCP Server
Empower your AI agent to orchestrate your marketing operations with Convertlab (DM Hub), the leading customer engagement and marketing automation platform in China. By connecting Convertlab to your agent, you transform complex customer segmentation, campaign tracking, and behavioral auditing into a natural conversation. Your agent can instantly list customers, retrieve detailed profile information, monitor marketing campaigns, and browse behavioral events without you ever needing to navigate the comprehensive DM Hub interface. Whether you are conducting a customer data audit or monitoring the performance of a high-volume campaign, your agent acts as a real-time marketing operations assistant, keeping your data accurate and your engagement moving.
Pydantic AI validates every Convertlab tool response against typed schemas, catching data inconsistencies at build time. Connect 8 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
- Customer Orchestration — List all DM Hub customers and retrieve detailed profile and membership information.
- Campaign Management — Browse active and historical marketing campaigns and retrieve detailed performance metadata.
- Event Auditing — List and retrieve detailed customer behavioral events to monitor engagement levels.
- Segmentation Control — Browse membership groups and identify customer segments for targeted activities.
- Operations Insights — Retrieve metadata about your marketing touchpoints and application status.
The Convertlab MCP Server exposes 8 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 Convertlab to Pydantic AI via MCP
Follow these steps to integrate the Convertlab 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 8 tools from Convertlab with type-safe schemas
Why Use Pydantic AI with the Convertlab MCP Server
Pydantic AI provides unique advantages when paired with Convertlab 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 Convertlab integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Convertlab connection logic from agent behavior for testable, maintainable code
Convertlab + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Convertlab MCP Server delivers measurable value.
Type-safe data pipelines: query Convertlab with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Convertlab tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Convertlab and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Convertlab responses and write comprehensive agent tests
Convertlab MCP Tools for Pydantic AI (8)
These 8 tools become available when you connect Convertlab to Pydantic AI via MCP:
create_customer
Create a new customer
get_campaign
Get campaign details
get_customer
Get customer details
list_campaigns
List marketing campaigns
list_customers
List DM Hub customers
list_events
List marketing events
list_member_groups
List customer segments
list_touchpoints
List marketing touchpoints
Example Prompts for Convertlab in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Convertlab immediately.
"List all my DM Hub customers."
"Show me the details for campaign 'Spring-2026'."
"List all customer segmentation groups."
Troubleshooting Convertlab MCP Server with Pydantic AI
Common issues when connecting Convertlab to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiConvertlab + Pydantic AI FAQ
Common questions about integrating Convertlab 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 Convertlab 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 Convertlab to Pydantic AI
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.