How to Use the NetEase Cloud Gaming MCP in LlamaIndex

Q: How do I filter NetEase Cloud Gaming tools in LlamaIndex?

Use the allowedtools filter when setting up your tool spec. This restricts the agent to only the functions you specify.

Index live NetEase Cloud Gaming API results into your LlamaIndex knowledge base for semantic retrieval.

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Connect NetEase Cloud Gaming MCP to LlamaIndex

Create your Vinkius account to connect NetEase Cloud Gaming 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 NetEase Cloud Gaming with LlamaIndex

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Index NetEase Cloud Gaming data with LlamaIndex

Transform the output of `list_games` into vector embeddings. This allows your agent to perform semantic searches over the entire game catalog. Your LlamaIndex pipeline stores the results of `get_game_detail` so you can query for specific genres or features later. The agent retrieves this data to answer questions based on current platform facts.

Ground your LlamaIndex agent in platform status

Run `get_session_status` and feed the results into your index. Your agent now knows exactly which sessions are active without guessing. Use `get_system_health` to keep your knowledge base updated on platform availability. If the system reports latency or downtime, your agent has that data indexed and ready for retrieval.

Manage server clusters through this MCP Server

Query `list_clusters` and index the cluster metadata for quick reference. Your LlamaIndex setup can then suggest the best server for a specific user location. Connect `get_cluster_info` to your RAG application to provide context-aware answers about infrastructure state. This keeps your agent informed on real-world capacity changes.

Setup guide

Set up NetEase Cloud Gaming 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 NetEase Cloud Gaming 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 NetEase Cloud Gaming tools.",
)
response = await agent.run("List recent NetEase Cloud Gaming 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 NetEase Cloud Gaming. 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 NetEase Cloud Gaming MCP in LlamaIndex

The tool output is converted into a structured format that your vector store consumes. You can then query these results as part of your document index.

Once you index the tool output, you can run natural language queries against your previous session data. This creates a searchable history of your interactions.

No, because the agent draws from your live index. If the tool returns data, your answer is grounded in that specific API response.

Use the allowed_tools filter when setting up your tool spec. This restricts the agent to only the functions you specify.

Your sensitive quota figures are processed in memory and never written to disk by the indexer. We isolate all API responses from your persistent vector storage.

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