How do I get a Dixa API Token?

Log in to your Dixa account as an administrator, navigate to **Settings > Integrations > API Tokens**, and click **Create Token**. Note that you may need a specific plan for API access.

Can the agent reply to customers?

This integration currently focuses on listing and auditing conversations, agents, and queues. Sending replies to customers should be managed via the Dixa agent interface.

Does the integration show real-time agent presence?

Yes, you can use the list_service_agents or quick_agent_presence_audit tools to see the current presence status of your support team.

Dixa MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Dixa
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Dixa through 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 Dixa "
            "(10 tools)."
        ),
    )

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

asyncio.run(main())

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* 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 Dixa MCP Server

Integrate Dixa, the customer friendship platform, directly into your AI workflow. Manage your multi-channel support conversations, monitor agent presence and performance, track service queues, and oversee your support teams using natural language.

Pydantic AI validates every Dixa tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through 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

Conversation Oversight — List and retrieve detailed information for all customer conversations and their current processing status.
Agent Intelligence — Monitor real-time agent presence, profile details, and team assignments across your organization.
Queue Monitoring — Track active service queues and routing settings to ensure efficient support delivery.
Team Management — List all support teams and identify members assigned to specific organizational units.

The Dixa MCP Server exposes 10 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 Dixa to Pydantic AI via MCP

Follow these steps to integrate the Dixa 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 10 tools from Dixa with type-safe schemas

Why Use Pydantic AI with the Dixa MCP Server

Pydantic AI provides unique advantages when paired with Dixa 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 Dixa integration code

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

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

Dixa + Pydantic AI Use Cases

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

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

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

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

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

Dixa MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Dixa to Pydantic AI via MCP:

get_agent_profile

Get full profile and performance data for a specific agent

get_conversation_details

Get detailed information for a specific customer conversation

get_service_account_metadata

Retrieve metadata and usage limits for your Dixa account

list_customer_conversations

List all customer service conversations in your Dixa account

list_open_support_tickets

Identify conversations that are currently in an "Open" or "Unassigned" status

list_service_agents

List all support agents registered in your Dixa organization

list_service_queues

List all active service queues configured in Dixa

list_support_teams

List all configured support teams and their members

quick_agent_presence_audit

Retrieve a high-level summary of active agent presence statuses

search_conversations_by_subject

Search for conversations using a keyword in the subject

Example Prompts for Dixa in Pydantic AI

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

"List all open support conversations."

"Show me the details for conversation '12345'."

"Who is currently available in the 'Sales' team?"

Troubleshooting Dixa MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Dixa + Pydantic AI FAQ

Common questions about integrating Dixa 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 Dixa MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.