How to Use the Gotify MCP in Pydantic AI

Q: Can I use Pydantic AI to list Gotify apps?

Yes. Use getapplications. The agent will map the returned JSON into typed objects for your code to use.

Type-safe Gotify notifications for Pydantic AI. Ensure your alerts arrive exactly as defined.

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Connect Gotify MCP to Pydantic AI

Create your Vinkius account to connect Gotify 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 Gotify with Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Type-safe messaging for Pydantic AI

Every `send_message` call is validated against a schema. If the payload is malformed, your agent stops before sending junk to your phone. This keeps your notification feed clean. You define the structure, and the agent enforces it at runtime.

Manage Gotify resources with Pydantic AI

Provision new applications with `create_application` and trust the return data. Pydantic models parse the response into objects you can use immediately. This removes the guesswork from API responses. You know exactly what the server returned because the model rejects anything else.

Clean up Gotify data with Pydantic AI

Automate your storage management by calling `delete_all_messages` when your queue gets too long. It keeps your server footprint small. Your agent handles the cleanup logic. You specify the conditions, and it executes the command safely.

Setup guide

Set up Gotify 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": {
        "gotify-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Gotify transactions")
print(result.output)

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Common questions about Gotify MCP in Pydantic AI

It works natively. You plug in the MCP server URL, and Pydantic AI validates every interaction between your agent and the Gotify API.

It does. Validation errors occur if the API returns unexpected fields. This prevents silent failures in your notification logic.

Yes. Use `get_applications`. The agent will map the returned JSON into typed objects for your code to use.

Keep it simple. Instantiate the toolset with your server URL, and the agent does the rest of the heavy lifting.

The server uses standard authentication. Your tokens are handled in-memory and never logged, keeping your notification credentials isolated from your application state.

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