How to Use the bluebarry.ai MCP in Pydantic AI

Use bluebarry.ai with Pydantic AI for type-safe customer support automations that never fail silently.

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Connect bluebarry.ai MCP to Pydantic AI

Create your Vinkius account to connect bluebarry.ai 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 bluebarry.ai with Pydantic AI

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Type-safe agent management for Pydantic AI

Tools like `list_agents` return data that is validated against your schema. If a field changes, Pydantic AI catches it immediately. You won't deal with corrupted agent records. The server forces a clean handshake with your logic.

Validate automation status data

Use `get_automation_status` to pull live metrics into your agent. Everything is checked against Pydantic models at runtime. If the server returns garbage, the agent stops before processing it. This keeps your state machine accurate.

Message customers with confidence

The `send_whatsapp` tool integrates perfectly into your type-safe pipelines. Your agent knows exactly what inputs the tool requires. It removes the guesswork from your messaging flow. You'll get a runtime error if your data doesn't match the expected format.

Setup guide

Set up bluebarry.ai 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": {
        "bluebarryai-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about bluebarry.ai MCP in Pydantic AI

You get runtime validation for every tool call. If the API returns unexpected data, the agent halts, preventing any downstream errors.

It does. Initialize your toolset with the HTTP endpoint and pass it to your Agent definition. It supports SSE and Streamable HTTP.

Since you define your own Pydantic models, the agent will validate the MCP server response against those models. It is highly flexible.

The server runs externally, providing a stable target for your agent. We recommend running the server in a managed container.

All communication is encrypted in transit and validated through strict schema enforcement. We never store your customer interaction logs, ensuring your data remains isolated and ephemeral.

Use it with your favorite AI tools

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

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