How to Use the Kameleoon MCP in Pydantic AI

Q: How do I add the Kameleoon MCP Server to Pydantic AI?

Install the slim MCP package and initialize the MCPToolset with your server URL. Pass this toolset into your agent to enable type-safe tool execution.

Q: Does Pydantic AI validate the Kameleoon experiment results?

Yes. Every tool response from getexperimentresults is checked against your Pydantic models. You get a clear validation error if the API payload doesn't match your expectations.

Q: Can I use Pydantic AI to list all Kameleoon experiments?

Absolutely. Call listexperiments and the agent will return a typed list of your current campaigns, ready for further processing or status monitoring.

Enforce strict types on your testing workflows with Pydantic AI and the Kameleoon MCP Server.

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Connect Kameleoon MCP to Pydantic AI

Create your Vinkius account to connect Kameleoon 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 Kameleoon with Pydantic AI

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Type-safe experiment creation with Pydantic AI

Use `create_experiment` knowing that Pydantic AI will validate the response structure immediately. If the API returns unexpected data, your agent halts before any bad state is saved, preventing configuration drift.

Analyze targeting rules in Pydantic AI

Call `list_targeting_rules` to retrieve your audience logic. The framework validates the schema of these rules against your defined models so you never work with malformed data.

Query site and variation data for Pydantic AI

Retrieve `list_variations` and `list_custom_data` with full confidence in the data types. Your agent works with clean, validated objects every time it interacts with the Kameleoon backend.

Setup guide

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

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

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

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Common questions about Kameleoon MCP in Pydantic AI

Install the slim MCP package and initialize the `MCPToolset` with your server URL. Pass this toolset into your agent to enable type-safe tool execution.

Yes. Every tool response from `get_experiment_results` is checked against your Pydantic models. You get a clear validation error if the API payload doesn't match your expectations.

The agent will raise a validation error immediately. This stops the execution and prevents your logic from processing corrupted or unexpected experiment data.

Absolutely. Call `list_experiments` and the agent will return a typed list of your current campaigns, ready for further processing or status monitoring.

The server is built to handle authentication via a single endpoint token. It only interacts with your experiment, site, and targeting metadata, keeping your account settings isolated and secure.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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