How to Use the Jebbit MCP in Pydantic AI

Q: Are there specific tools in Jebbit that Pydantic AI handles best?

All tools like listwebhooks and getaccount are compatible. Pydantic AI excels when you use it to validate the more complex outputs like nested segment attributes.

Strictly validate Jebbit data in your Pydantic AI agents. No silent errors, just type-safe interaction with your zero-party data.

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Connect Jebbit MCP to Pydantic AI

Create your Vinkius account to connect Jebbit 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 Jebbit with Pydantic AI

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Type-safe Jebbit access in Pydantic AI

Every response from `list_attributes` or `list_users` is validated against your Pydantic models. If the API returns unexpected fields, your agent stops immediately. This prevents corrupted data from entering your agent logic. You get a clear validation error rather than a silent failure during execution.

Audit Jebbit campaigns using Pydantic AI

Use `list_campaigns` and `list_experiences` to keep your agent informed about current marketing efforts. Because the output is mapped to your types, you can trust the data structure. Your agent can now reliably map campaign IDs to specific logic branches. It removes the guesswork from handling dynamic platform data.

Track Jebbit segments with Pydantic AI

Identify audience cohorts using `list_segments` with full type safety. The agent knows exactly what the segment data looks like before it makes a decision. This approach is ideal for agents that need to handle complex personalization rules. You avoid runtime errors caused by missing or malformed segment attributes.

Setup guide

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

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

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

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Common questions about Jebbit MCP in Pydantic AI

You define your Pydantic models to match the expected API output. When the Pydantic AI agent invokes a tool like `get_experience`, the framework validates the response against your schema automatically.

Your agent will raise a validation error during the tool response phase. This prevents the model from hallucinating or working with invalid data from `list_reporting_jobs`.

Yes, the server supports SSE and Streamable HTTP. You configure the `MCPToolset` with your endpoint URL, and Pydantic AI manages the connection.

All tools like `list_webhooks` and `get_account` are compatible. Pydantic AI excels when you use it to validate the more complex outputs like nested segment attributes.

The server enforces token-based authentication for every request. By using Pydantic models, you also create a strict boundary for what data your agent is allowed to process, ensuring only defined zero-party data types are handled.

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