How to Use the WorkAdventure MCP in LlamaIndex

Q: How does WorkAdventure work with LlamaIndex?

You run MCP tools (like listrooms) and index the results. This turns ephemeral API data into searchable, long-term knowledge for your application.

Q: Can I query past member status using WorkAdventure and LlamaIndex?

Absolutely. By indexing historical calls to getmemberdetails, you can search previous records to find out a member's virtual location or role at any point in time.

Q: Does the MCP Server handle data privacy for WorkAdventure and LlamaIndex?

The server touches member details and room variables. When indexing, you control what is stored in the vector store to maintain user privacy.

Q: What's the best way to use listmaps with LlamaIndex?

Call listmaps, index that list of names and IDs, and then allow users to query about map availability without needing a direct API call.

Q: What kind of data types does this MCP Server touch for WorkAdventure and LlamaIndex?

The primary data types are map details, room variables (key-value pairs), and specific member IDs. Indexing these allows deep historical context.

Build searchable knowledge bases using LlamaIndex with WorkAdventure data.

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Connect WorkAdventure MCP to LlamaIndex

Create your Vinkius account to connect WorkAdventure to LlamaIndex 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 LlamaIndex

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Create a searchable log of member movements.

Don't just run a tool; index the results. Your RAG application can use `list_members` to get names and IDs, and then repeatedly call `get_member_details`. By indexing these outputs, you build a knowledge base that answers questions like 'What was Jane’s status last week?'—grounded in actual API data. This means your agent retrieves context from past sessions about who used which virtual resources.

Query historical map and room configurations.

LlamaIndex lets you index the output of `get_map_details` or `list_maps`. This allows a user to query, 'What were the setup requirements for the Alpha team's virtual office?' The answer comes from the indexed documentation of the map details, not just what the API returns right now. It turns live configuration data into persistent, semantic knowledge.

Build a unified index from room variables.

If you run `get_room_variables` for several rooms over time, LlamaIndex indexes those key-value pairs. You can then ask, 'Which rooms were marked as high priority last month?' The system searches the indexed variable data to find records matching that description. It merges the live state of the world with your historical search capability.

Setup guide

Set up WorkAdventure MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all WorkAdventure MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to WorkAdventure tools.",
)
response = await agent.run("List recent WorkAdventure data")

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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 LlamaIndex

You run MCP tools (like `list_rooms`) and index the results. This turns ephemeral API data into searchable, long-term knowledge for your application.

Absolutely. By indexing historical calls to `get_member_details`, you can search previous records to find out a member's virtual location or role at any point in time.

The server touches `member details` and `room variables`. When indexing, you control what is stored in the vector store to maintain user privacy.

Call `list_maps`, index that list of names and IDs, and then allow users to query about map availability without needing a direct API call.

The primary data types are `map details`, `room variables` (key-value pairs), and specific `member IDs`. Indexing these allows deep historical context.

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