How to Use the Chameleon.io MCP in Pydantic AI

Get type-safe product adoption data in Pydantic AI with this Chameleon.io MCP Server.

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Connect Chameleon.io MCP to Pydantic AI

Create your Vinkius account to connect Chameleon.io 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 Chameleon.io with Pydantic AI

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Type-safe experience management in Pydantic AI

Use `list_experiences` to get a validated list of your active tours. Pydantic AI checks the response against your defined models. Check `get_experience_details` for specific tour metadata. If the API returns junk, the agent halts before acting.

Verified user data for Pydantic AI

Call `identify_chameleon_user` to update your user state. The tool ensures the schema matches your Pydantic definitions perfectly. Use `delete_chameleon_user` to purge records. This operation is strictly typed to prevent accidental data loss.

Reliable event logging in Pydantic AI

Track user actions with `track_user_event`. Your agent verifies every field type before the server processes the request. Review event logs via `list_chameleon_events`. The framework catches any unexpected fields, preventing silent failures in your logic.

Setup guide

Set up Chameleon.io 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": {
        "chameleonio-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Chameleon.io MCP in Pydantic AI

Every tool call maps to a Pydantic model. If the server returns data that doesn't match, the framework raises a validation error immediately.

Yes, it is model-agnostic. Your agent manages the communication with the server regardless of which underlying model you choose.

Install the slim package, define your toolset with the server URL, and pass it to your agent instance.

It supports SSE transports for real-time interaction. You can stream tool results back to your agent as they arrive.

Yes, data stays within your local agent environment. You dictate the validation rules, keeping your user segments secure from external leakage.

Use it with your favorite AI tools

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

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