How to Use the Google Pub/Sub Topic MCP in Pydantic AI

Type-safe publishing: Google Pub/Sub Topic with Pydantic AI.

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Connect Google Pub/Sub Topic MCP to Pydantic AI

Create your Vinkius account to connect Google Pub/Sub Topic to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Google Pub/Sub Topic with Pydantic AI

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Guaranteed Message Structure

The most important thing here is correctness. When your agent decides to publish an event, the Pydantic AI framework validates that message against a strict Python model before calling `pubsub_publish_message`. This means no hallucinated fields get published. If the data structure fails validation, the agent doesn't proceed; it throws a clear error. You eliminate silent corruption entirely.

Model-Agnostic Reliability

The Pydantic AI approach works regardless of which LLM you use—OpenAI, Anthropic, or Gemini. The validation layer sits *on top* of the MCP call, making it a universal safeguard. You get the power of cloud event triggering without sacrificing data quality control at runtime.

Runtime Validation Feedback

If the API returns unexpected data for your publishing task—say, a required field is missing or misspelled—the agent fails *loudly*. You get a validation error immediately. This precise feedback loop lets you debug faulty logic without having to check logs hours later.

Setup guide

Set up Google Pub/Sub 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": {
        "google-pubsub-topic-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Google Pub/Sub Topic tools.",
)

result = await agent.run("List recent Google Pub/Sub Topic transactions")
print(result.output)

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Common questions about Google Pub/Sub Topic MCP in Pydantic AI

The Pydantic framework validates the message payload against a defined Python model *before* it's sent. This means you are guaranteed that the data structure matches what your subscribing services expect.

Yes, significantly. By enforcing type safety at the tool call level, it prevents runtime failures caused by unexpected data formats. This makes your entire agent pipeline much more dependable.

The system rejects it immediately. Since the framework requires a defined model for validation, any input that doesn't fit the expected schema will cause an explicit failure.

Yes. The toolset is model-agnostic. As long as your agent can call the MCP, the validation layer works equally well whether you're running on OpenAI, Gemini, or a local system.

It handles strictly typed payloads. Your agent must define the exact fields and data types for the message, ensuring that only clean, structured data enters the topic.

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