Mailingwork MCP Server for Pydantic AIGive Pydantic AI instant access to 10 tools to Create Subscriber, Get Mailing, Get Subscriber, and more
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Mailingwork through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
Ask AI about this App Connector for Pydantic AI
The Mailingwork app connector for Pydantic AI is a standout in the Marketing Automation category — giving your AI agent 10 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Mailingwork "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in Mailingwork?"
)
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 Mailingwork MCP Server
Connect your Mailingwork account to any AI agent and manage email campaigns through natural conversation.
Pydantic AI validates every Mailingwork 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
- Campaign Management — Create, schedule, and track email campaigns
- Subscriber Lists — Manage mailing lists with import and segmentation
- Report Analytics — Access open rates, click maps, and delivery metrics
- Deliverability — Monitor bounce rates and sender reputation
- Template Management — Browse and manage email templates
The Mailingwork 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.
All 10 Mailingwork tools available for Pydantic AI
When Pydantic AI connects to Mailingwork through Vinkius, your AI agent gets direct access to every tool listed below — spanning gdpr-compliant, campaign-management, subscriber-segmentation, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Create a new subscriber
Get mailing details
Get subscriber details
List all subscriber lists
List all mailings/campaigns
List all subscribers
List all tags
g., order confirmation). Send a transactional email
Trigger an automated workflow
Update an existing subscriber
Connect Mailingwork to Pydantic AI via MCP
Follow these steps to wire Mailingwork into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install Pydantic AI
pip install pydantic-aiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use Pydantic AI with the Mailingwork MCP Server
Pydantic AI provides unique advantages when paired with Mailingwork 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 Mailingwork integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Mailingwork connection logic from agent behavior for testable, maintainable code
Mailingwork + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Mailingwork MCP Server delivers measurable value.
Type-safe data pipelines: query Mailingwork with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Mailingwork tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Mailingwork and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Mailingwork responses and write comprehensive agent tests
Example Prompts for Mailingwork in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Mailingwork immediately.
"Show all campaigns and performance for this month."
"Show mailing lists and subscriber growth."
"Show click map and deliverability report for the Spring Newsletter."
Troubleshooting Mailingwork MCP Server with Pydantic AI
Common issues when connecting Mailingwork to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiMailingwork + Pydantic AI FAQ
Common questions about integrating Mailingwork 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.