How to Use the Drip MCP in Pydantic AI

Q: How do I register the Drip MCP Server in Pydantic AI?

Run pip install "pydantic-ai-slimmcp". Import MCPToolset with your Vinkius HTTP URL, and pass it directly to your Agent instance in the toolsets list.

Q: Does Pydantic AI prevent invalid Drip subscriber data?

Yes. If a tool like createsubscriber or tagsubscriber returns data that violates your Pydantic models, the framework raises a validation error, stopping the agent from acting on corrupted inputs.

Q: Can I use Pydantic AI to run Drip workflows?

Yes, your agent can discover and invoke startworkflow and listworkflows to manage subscriber paths. Every payload is strictly validated against the server's schema before execution.

Build type-safe marketing agents with Pydantic AI and run verified Drip subscriber and workflow operations.

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Connect Drip MCP to Pydantic AI

Create your Vinkius account to connect Drip to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Drip with Pydantic AI

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Type-Safe Subscriber Updates

Manage subscriber profiles using `create_subscriber` and `get_subscriber` with strict runtime validation. Ensure every subscriber update matches your schemas before hitting your live account using this MCP Server. Your Pydantic AI agent manages profiles, validating every field against strict Python types to eliminate formatting errors. If the API returns unexpected custom fields, the framework raises a validation error immediately rather than corrupting your CRM. This strict validation protects your lists when using `list_subscribers` to audit active customer records.

Tag and Workflow Execution

Control automated marketing paths using `list_workflows` and `start_workflow` without silent failures. The agent checks your active setup and safely triggers entry steps, validating the payload structure at runtime. You can quickly modify user categories using `tag_subscriber` and `remove_tag`. Because the framework is model-agnostic, you can swap your LLM provider while retaining identical, validated execution schemas for all tag operations.

Validated Event Tracking with this MCP Server

Track custom conversions with absolute structural integrity using `record_event`. Your agent executes this tool to log user milestones, ensuring every custom property conforms exactly to your Pydantic models. The agent reads active tags using `list_tags` and checks email broadcast lists via `list_campaigns`. This ensures your agent never references non-existent assets, keeping your automated marketing loops clean and error-free.

Setup guide

Set up Drip MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "drip-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Drip tools.",
)

result = await agent.run("List recent Drip transactions")
print(result.output)

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Common questions about Drip MCP in Pydantic AI

Run `pip install "pydantic-ai-slim[mcp]"`. Import `MCPToolset` with your Vinkius HTTP URL, and pass it directly to your `Agent` instance in the `toolsets` list.

Yes. If a tool like `create_subscriber` or `tag_subscriber` returns data that violates your Pydantic models, the framework raises a validation error, stopping the agent from acting on corrupted inputs.

This MCP Server supports both Streamable HTTP and SSE transports. Use the unified `MCPToolset` class to connect to your externally running Vinkius endpoint.

Yes, your agent can discover and invoke `start_workflow` and `list_workflows` to manage subscriber paths. Every payload is strictly validated against the server's schema before execution.

Your Drip API keys and account IDs are never exposed to the LLM. They are held securely in Vinkius environment variables, while the runtime executes all subscriber and tag tools inside isolated, zero-trust V8 sandboxes.

Use it with your favorite AI tools

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