How to Use the GetResponse MCP in Pydantic AI

Q: Does Pydantic AI validate GetResponse campaign data?

Yes. When the agent calls getcampaigndetails, the response runs through your Pydantic models. Any schema mismatch throws an immediate validation error.

Enforce strict type safety for your GetResponse API calls using Pydantic AI and this dedicated MCP server.

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Connect GetResponse MCP to Pydantic AI

Create your Vinkius account to connect GetResponse 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 GetResponse with Pydantic AI

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Strict subscriber validation

The `add_new_subscriber` tool executes only when your Pydantic models validate the exact payload. If the agent hallucinates a missing email field, the framework fails loudly before hitting the API. You use `list_marketing_contacts` and `get_contact_details` to verify existing records. The type checker ensures every contact ID matches the expected string format, preventing silent data corruption.

Parse analytics with Pydantic AI

The `get_newsletter_analytics` tool forces the incoming broadcast metrics to match your predefined data schemas. You never deal with unexpected null values breaking your downstream reporting. Your agent cross-references those stats by running `list_marketing_newsletters`. It builds a structured, validated report that you can trust in production.

Validate your MCP Server workflows

The `list_marketing_workflows` and `list_marketing_webhooks` tools expose your automation logic to the agent. It maps the dependencies and validates them against your expected infrastructure state. Before any of this runs, `verify_api_connection` ensures the endpoint is actually alive. You catch network failures immediately instead of debugging mysterious timeout errors later.

Setup guide

Set up GetResponse 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": {
        "getresponse-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about GetResponse MCP in Pydantic AI

Use MCPToolset with your HTTP endpoint. Pass it via the toolsets array to your Agent. Do not use the deprecated HTTP server class.

Yes. When the agent calls `get_campaign_details`, the response runs through your Pydantic models. Any schema mismatch throws an immediate validation error.

You can. The framework is model-agnostic, meaning your local LLM can trigger the tools just like OpenAI or Anthropic would.

Pydantic AI catches it instantly. The strict runtime typing means your agent halts execution rather than pushing malformed data into your database.

The server accesses infrastructure metadata like webhook URLs and automation flow IDs. Authentication is handled entirely off-client, requiring just one secure token to access the managed execution environment.

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