How to Use the WorkAdventure MCP in Pydantic AI

Q: Can Pydantic AI manage users in WorkAdventure?

Yes. You can add (createmember) or remove (deletemember) members while having the framework validate that all associated user details and IDs are correct before execution within WorkAdventure.

Ensure absolute data correctness when managing WorkAdventure using Pydantic AI.

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Connect WorkAdventure MCP to Pydantic AI

Create your Vinkius account to connect WorkAdventure 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 WorkAdventure with Pydantic AI

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Managing Member Presence with MCP Server

When you call `create_member`, the agent knows exactly what structure the new member must conform to. If the API sends bad data, your system fails loudly, telling you exactly which field broke. Similarly, when removing a person via `delete_member`, you’re guaranteed that the input parameters match the expected schema for WorkAdventure.

Retrieving and Validating World Context

Using `get_world_info` gives your agent structured data about the entire work environment. The Pydantic framework validates this output, so you never have to worry about unexpected or missing fields. Check available rooms with `list_rooms`. Every room list returned is typed and validated before your process continues.

Fine-Tuning Room Variables

The `update_room_variable` tool allows the agent to set specific variables in a room. Crucially, Pydantic validates that this update attempt uses correct data types and formats. You can also query existing context with `get_room_variables`, knowing instantly that the returned data structure is reliable and type-safe.

Setup guide

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

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

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

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Common questions about WorkAdventure MCP in Pydantic AI

It wraps every MCP call, including those for WorkAdventure. If any tool returns data that doesn't match a defined Python model, your agent stops immediately with an error. No silent failures.

Yes. You can add (`create_member`) or remove (`delete_member`) members while having the framework validate that all associated user details and IDs are correct before execution within WorkAdventure.

The primary guarantee is correctness. Whether you’re getting map details or room variables, the agent only processes data that has been strictly validated against a type-safe model.

You'll get structured access to global environment metrics. The agent uses `get_world_info` and the resulting data is guaranteed to conform to a predefined schema, making it safe for production code.

The server exposes structured primitives like map details (via `get_map_details`) and member identifiers. The Pydantic layer ensures these specific data structures are always reliable.

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