How to Use the WorkAdventure MCP in LangChain

Q: How do I use WorkAdventure and LangChain together?

You build a multi-step pipeline. Your agent calls listmaps first, and then uses the map name returned as input to call getmapdetails. It’s all about chaining inputs to outputs.

Q: Can LangChain track user activity in WorkAdventure?

Yes. You can chain together calls like listmembers and then getmemberdetails multiple times. This lets your agent build a profile of who’s logged in and what their current variables are.

Q: If I change room settings, how does WorkAdventure interact with LangChain?

You run updateroomvariable via the chain. The success or failure of that update becomes a measurable output for your agent to use in its next decision step.

Q: What if I need current world data from WorkAdventure with LangChain?

Just start by calling getworldinfo. This foundational piece of data can then feed into subsequent calls, like checking the capacity using tools that list members.

Build multi-step reasoning agents for WorkAdventure using LangChain.

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Connect WorkAdventure MCP to LangChain

Create your Vinkius account to connect WorkAdventure to LangChain 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 LangChain

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ChatGPT

Claude

Perplexity

Orchestrate member management in WorkAdventure.

Need to onboard a new team member? Your agent executes a chain: first, it calls `get_world_info` to check capacity limits. Then, it uses the `create_member` tool and passes the resulting ID into a subsequent step that updates their initial room variable using `update_room_variable`. This allows you to build full onboarding pipelines. This sequence ensures the member actually exists before your agent tries to assign them a specific role or location. It’s pure, traceable logic flow.

Map out virtual spaces and check occupancy.

Your ReAct agent can navigate complex world data. Start by listing all available locations with `list_maps`. Once you find a map ID that looks promising, the chain calls `get_map_details` to confirm its features. Finally, it gets an accurate headcount using `list_members`, giving your user immediate situational awareness. It handles this as one cohesive sequence: Check available options -> Verify details of option X -> Report current usage.

Track room states and variables dynamically.

The agent can pinpoint exactly what's going on in any given virtual space. It first calls `list_rooms` to get a list of active zones. Then, it uses the specific room ID to run `get_room_variables`. If the variable indicates an issue (like 'needs maintenance'), your chain immediately pivots and runs `get_world_info` to find alternative locations. This means you build decision trees where the agent doesn't just call tools; it reasons about which tool is needed next based on the output.

Setup guide

Set up WorkAdventure MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes WorkAdventure tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "workadventure-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent WorkAdventure transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by WorkAdventure. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about WorkAdventure MCP in LangChain

You build a multi-step pipeline. Your agent calls `list_maps` first, and then uses the map name returned as input to call `get_map_details`. It’s all about chaining inputs to outputs.

Yes. You can chain together calls like `list_members` and then `get_member_details` multiple times. This lets your agent build a profile of who’s logged in and what their current variables are.

You run `update_room_variable` via the chain. The success or failure of that update becomes a measurable output for your agent to use in its next decision step.

Just start by calling `get_world_info`. This foundational piece of data can then feed into subsequent calls, like checking the capacity using tools that list members.

The server handles structured data regarding virtual locations. Specifically, you'll be reading and writing `room variables` (key-value pairs) and managing `member IDs`.

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