How to Use the Azure Service Bus Topic MCP in Pydantic AI

Validate and send events to Azure Service Bus Topics using Pydantic AI's type-safe framework.

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Connect Azure Service Bus Topic MCP to Pydantic AI

Create your Vinkius account to connect Azure Service Bus Topic 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 Azure Service Bus Topic with Pydantic AI

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Type-validated publishing for Pydantic AI

Every message sent via `publish_message` follows your defined Pydantic models. If the agent tries to send malformed data, the framework catches it immediately. This prevents bad data from ever hitting your bus. You define the schema, and the agent must comply before the call succeeds.

Strict metadata control

You use Pydantic models to enforce the structure of your customProperties. This ensures your routing metadata is always valid before reaching the bus. It stops errors before they happen. Your agent cannot accidentally send invalid routing headers to your subscriptions.

Reliable event delivery

The MCP server integrates directly into the agent's toolset. It works with whatever model you choose, keeping your logic portable. You keep the power of runtime validation while gaining the ability to trigger cloud events. It's the standard way to bridge type-safe agents to your infrastructure.

Setup guide

Set up Azure Service Bus Topic 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": {
        "azure-service-bus-topic-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Azure Service Bus Topic transactions")
print(result.output)

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Common questions about Azure Service Bus Topic MCP in Pydantic AI

You instantiate an MCPToolset with the server URL and add it to your agent's toolsets. This allows the agent to use the messaging tools within your type-safe pipeline.

The agent will raise a validation error before the tool is even invoked. This keeps your bus clean of corrupt or malformed message payloads.

You can utilize all available routing metadata via customProperties. The framework ensures your input matches the expected schema for those headers.

You can. Pydantic AI handles multiple toolsets, allowing you to combine messaging capabilities with other data sources in a single agent.

The server operates in an ephemeral sandbox. It only processes the specific message objects validated by your agent, and it never logs your sensitive business data.

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