How to Use the Kissmetrics MCP in Pydantic AI

Get type-safe analytics in Pydantic AI. Use Kissmetrics tools with strict model validation to keep your data clean.

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Connect Kissmetrics MCP to Pydantic AI

Create your Vinkius account to connect Kissmetrics 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 Kissmetrics with Pydantic AI

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Strictly validate Kissmetrics data

Pydantic AI checks every response from the API against your defined models. If the data format shifts, your agent catches it immediately. This prevents bad data from polluting your analytics logs. You get a clear validation error instead of silent corruption.

List property names in Pydantic AI

Know exactly what data fields exist in your account. Use `list_property_names` to fetch a list of all active properties for your agent to reference. This keeps your queries precise. Your agent won't waste time trying to access properties that haven't been defined yet.

Record behavior events with Pydantic AI

Log user actions with confidence. Every call to `record_event` is validated against your schema to ensure the identity and event name are correct. This gives you a reliable audit trail for your product decisions. It removes the guesswork from your event logging process.

Setup guide

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

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Kissmetrics. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

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Common questions about Kissmetrics MCP in Pydantic AI

If the API returns a response that doesn't match your Pydantic model, the agent throws a validation error. This forces you to handle the data issue before your logic proceeds.

Yes, simply point the MCPToolset to your local server instance. It works exactly like the production endpoint for testing your agent's responses.

You get all seven tools including identity aliasing and metric querying. Each one is automatically wrapped in validation logic when you add the toolset to your agent.

Pass the server endpoint to the MCPToolset constructor and add it to your agent. The framework handles the rest, ensuring your tool calls are type-checked.

All your calls are encrypted in transit and routed through our isolated V8 sandbox. We never log your raw event data or user properties, keeping your proprietary information strictly between you and Kissmetrics.

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