How do I trigger a specific Flow XO workflow using the agent?

You can use the 'trigger_webhook' tool. Provide the Webhook URL generated by your Flow XO 'Webhook Trigger' and a JSON payload. The agent will post the data to that URL, starting the automated flow remotely.

What is a 'response path' and how do I get it?

A response path is a unique identifier for a user's conversation on a specific platform. You can find it by using the 'list_chatbot_users' or 'get_user_details' tools. It's required for sending push messages via the 'send_push_message' tool.

Can I disable a chatbot flow temporarily using the agent?

Yes! The 'toggle_workflow' tool allows you to enable or disable any of your flows by providing the Workflow ID and setting the 'active' parameter to true or false.

Flow XO MCP Server for Pydantic AI 12 tools — connect in under 2 minutes

Name: Flow XO
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 12 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Flow XO through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Flow XO "
            "(12 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Flow XO?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Flow XO MCP Server

Connect your Flow XO account to any AI agent and automate your chatbot interactions and messaging workflows through the Model Context Protocol (MCP). Flow XO is a versatile platform for building and managing chatbots across various channels like Slack, Telegram, and the web. Now, you can manage your automation flows, oversee chatbot users, and trigger webhook-based workflows directly through natural conversation.

Pydantic AI validates every Flow XO tool response against typed schemas, catching data inconsistencies at build time. Connect 12 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Workflow Management — List all your chatbot flows and toggle their active status (enable/disable) instantly.
User Oversight — Access your end-user database, fetch detailed profiles, and create or update user records.
Direct Messaging — Send push messages directly to users via their unique response paths from your chat interface.
Webhook Triggers — Push data payloads to Flow XO webhook trigger URLs to start automated sequences remotely.
Interaction History — Retrieve the message history for specific users to understand past bot engagements.
Platform Connectivity — List all connected bot accounts and platforms (Slack, Messenger, etc.) for better integration context.
Automation Analytics — Fetch high-level usage summaries and performance metrics for your chatbot environment.

The Flow XO MCP Server exposes 12 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Flow XO to Pydantic AI via MCP

Follow these steps to integrate the Flow XO MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 12 tools from Flow XO with type-safe schemas

Why Use Pydantic AI with the Flow XO MCP Server

Pydantic AI provides unique advantages when paired with Flow XO through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Flow XO integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Flow XO connection logic from agent behavior for testable, maintainable code

Flow XO + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Flow XO MCP Server delivers measurable value.

Type-safe data pipelines: query Flow XO with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Flow XO tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Flow XO and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Flow XO responses and write comprehensive agent tests

Flow XO MCP Tools for Pydantic AI (12)

These 12 tools become available when you connect Flow XO to Pydantic AI via MCP:

create_user

get_automation_analytics

Get usage summary

get_user_details

Get user profile

list_bot_accounts

). List platform accounts

list_broadcasts

List sent broadcasts

list_chatbot_users

List all end users

list_user_history

List user messages

list_workflows

List automation flows

send_push_message

Send a push message

toggle_workflow

Enable/Disable a flow

trigger_webhook

Trigger flow via webhook

update_user

Update user metadata

Example Prompts for Flow XO in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Flow XO immediately.

"List all my Flow XO chatbot users."

"Disable the workflow 'Old Customer Survey'."

"Send a push message to path 'abc/123': 'Your order has been shipped!'."

Troubleshooting Flow XO MCP Server with Pydantic AI

Common issues when connecting Flow XO to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Flow XO + Pydantic AI FAQ

Common questions about integrating Flow XO MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Flow XO MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.