How to Use the NetEase BUFF MCP in LlamaIndex

Index your NetEase BUFF market data in LlamaIndex to build context-aware trading agents that learn from price trends.

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Connect NetEase BUFF MCP to LlamaIndex

Create your Vinkius account to connect NetEase BUFF 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 BUFF with LlamaIndex

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Index market trends for LlamaIndex

Turn raw price data from `get_price_history` into a searchable knowledge base. LlamaIndex ingests these tool outputs so your agent can query historical trends before making a trade. Querying your indexed market data allows for smarter decisions than simple rules-based scripts. You get an agent that understands the context of a skin's price movement.

Query inventory via LlamaIndex tools

Use `get_user_inventory` to feed your current holdings into a vector store. This lets your agent answer questions about your portfolio composition without re-fetching data every time. Your agent maintains an up-to-date view of your assets. It combines this inventory context with external market data to identify which items to hold or sell.

Automate order management via index

Implement `create_buy_order` as a tool that your agent calls based on indexed market signals. The agent evaluates the opportunity against your existing inventory knowledge. Keep your market presence tidy by using `get_buy_orders` to audit your status. If the market shifts, your agent uses `cancel_order` to protect your balance.

Setup guide

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

You wrap the MCP tools into a tool spec and pass them to your agent. This allows the framework to index tool outputs alongside your documents.

The performance depends on your index size and query frequency. Fetching data from the server is fast, but keep your indexing strategy lean to avoid latency.

Yes. All data retrieved from the market is scoped to your session. Vinkius ensures that the server does not persist your sensitive trading information.

Once you index the tool outputs, you can perform semantic searches across your trading history. This helps you find patterns in past successful transactions.

The server uses a tokenized handshake to communicate with the API. Your credentials never touch the client, keeping your account data behind a secure, ephemeral barrier.

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