How to Use the Flow XO MCP in Pydantic AI

Q: How does Pydantic AI validate the responses from Flow XO?

Every tool response, including user lists from listchatbotusers, is validated against strict Pydantic models at runtime. If the API schema changes, your code catches it instantly.

Q: Can I use Flow XO to send push notifications with Pydantic AI?

Yes, your agent can call sendpushmessage to send direct notifications. The payload is checked before sending to ensure your messages always format correctly.

Q: What is the best way to track sent broadcasts?

Use listbroadcasts to get a structured history of your sent messages. Your agent can parse this data to verify which campaigns went out successfully.

Build type-safe chatbot automations and manage users with the Flow XO integration for Pydantic AI using this MCP Server.

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Connect Flow XO MCP to Pydantic AI

Create your Vinkius account to connect Flow XO 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 Flow XO with Pydantic AI

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Type-safe workflow execution and webhook triggers

The `trigger_webhook` tool allows your agent to start automation flows with strict runtime validation. Every payload passed to the webhook is checked against Pydantic schemas before it leaves your application. You can also use `toggle_workflow` to change the status of your automations. If the API returns unexpected data, your agent raises a validation error immediately instead of failing silently.

Structured user profiling and history checks

The `get_user_details` tool fetches complete profile information and parses it into typed Python objects. Your agent can inspect these fields with absolute confidence that the data matches your models. To see what your users have been saying, use `list_user_history` to pull recent messages. If you need to update their details, `update_user` lets you modify their records with type-checked parameters.

Monitor bot fleets using this Pydantic AI MCP Server

The `list_bot_accounts` tool lists your active chat platforms and maps them directly to validated schemas. This makes it easy to write clean logic that manages multiple bot setups without runtime surprises. For high-level monitoring, `get_automation_analytics` pulls usage summaries that your agent can process or display. It gives you an accurate, type-safe view of your system's health.

Setup guide

Set up Flow XO 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": {
        "flow-xo-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Flow XO 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 Flow XO. 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.

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Common questions about Flow XO MCP in Pydantic AI

Instantiate the MCPToolset with your server's HTTP endpoint. Pass this toolset into your Agent constructor to automatically register the validated tools. This registers the MCP Server tools.

Every tool response, including user lists from `list_chatbot_users`, is validated against strict Pydantic models at runtime. If the API schema changes, your code catches it instantly.

Yes, your agent can call `send_push_message` to send direct notifications. The payload is checked before sending to ensure your messages always format correctly.

Use `list_broadcasts` to get a structured history of your sent messages. Your agent can parse this data to verify which campaigns went out successfully.

Your workflow configurations, user history logs, and API tokens are processed in a zero-trust, ephemeral V8 sandbox. No data is stored, and all connections use secure, token-based authentication.

Use it with your favorite AI tools

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