How to Use the ProcessOn MCP in Pydantic AI

Use Pydantic AI with ProcessOn. Type-safe diagram management with runtime validation for your Python agent.

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Connect ProcessOn MCP to Pydantic AI

Create your Vinkius account to connect ProcessOn 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 ProcessOn with Pydantic AI

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Type-safe diagram control for Pydantic AI

Define your agent's interactions with strict schemas. When you call `create_file` or `delete_file`, Pydantic AI checks the output against your models at runtime. This prevents silent errors. If the ProcessOn API returns unexpected data, your agent stops immediately, protecting your file system from bad inputs.

Validate ProcessOn responses in Pydantic AI

Every tool response is parsed into a model. Use `get_file` or `list_collaborators` to pull data into your agent while knowing exactly what structure to expect. Your code stays clean and readable. You don't have to write manual checks for every field because the framework handles the validation automatically.

Manage organizations with Pydantic AI agents

Fetch your organizational details using `get_org_info` and map them to your agent's logic. This gives your agent a clear view of your workspace constraints. It makes your agent reliable. By treating the response as a typed object, you build stable automation that handles diagram management without hallucinating fields.

Setup guide

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

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

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

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Common questions about ProcessOn MCP in Pydantic AI

Yes. Since you use the MCPToolset, the framework automatically validates every tool response. This includes complex outputs from `list_files` and `get_folder_content`.

Install the slim MCP package and initialize the MCPToolset with your Vinkius HTTP URL. Pass the toolset into your Agent to start using the tools.

The agent will raise a validation error immediately. This stops the execution flow before the error can propagate through your system.

Yes. The framework is model-agnostic. You can connect local models or hosted APIs to your ProcessOn server as long as they support the MCP protocol.

The server runs in an ephemeral Vinkius sandbox. Your diagram metadata is validated by Pydantic schemas in memory and never persisted in an insecure state.

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