How to Use the Cyberimpact MCP in Pydantic AI

Q: How do I set up Cyberimpact for Pydantic AI?

You install the pydantic-ai-slimmcp package and initialize an MCPToolset with your server URL. This allows your agent to use the provided tools while enforcing strict schema validation.

Enforce strict data validation for Cyberimpact operations using Pydantic AI.

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Connect Cyberimpact MCP to Pydantic AI

Create your Vinkius account to connect Cyberimpact 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 Cyberimpact with Pydantic AI

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Perplexity

Type-safe subscriber profile management

Your agent calls `get_subscriber_details` and receives a response that Pydantic AI validates against your models. If the API returns unexpected fields, the agent stops immediately to prevent errors. This approach ensures that your subscriber data structures remain consistent. You get complete reliability when your agent reads interaction histories or group memberships.

Automated campaign and template lookups

Fetch available templates with `list_message_templates` and verify campaign statuses using `list_email_campaigns`. Pydantic AI checks every response to ensure the data matches your expected schemas. Your agent avoids hallucinating fields because the validation happens at runtime. It keeps your marketing content management flow predictable and bug-free.

Query subscriber lists with confidence

Use `search_subscribers_by_email` to find specific profiles and trust the output because it's validated by your Pydantic models. This removes the risk of silent corruption when handling user data. Your agent now operates with clear constraints on what it can read from the subscriber database. It provides a robust foundation for building autonomous marketing tasks.

Setup guide

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

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

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

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Common questions about Cyberimpact MCP in Pydantic AI

You install the pydantic-ai-slim[mcp] package and initialize an MCPToolset with your server URL. This allows your agent to use the provided tools while enforcing strict schema validation.

Yes, if the API returns a response that doesn't fit your Pydantic model, the framework raises a validation error. This prevents your agent from making decisions based on bad data.

It is model-agnostic, meaning you can swap between OpenAI, Anthropic, or local models. The validation logic remains consistent regardless of the underlying LLM.

We operate a zero-trust environment where data is processed in memory. Your sensitive subscriber information is handled only during the execution of your agent's task.

It only touches the subscriber database boundary and messaging analytics. Your Pydantic AI agent only sees the specific fields you define in your schemas.

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