How to Use the NetEase BUFF MCP in Pydantic AI

Build type-safe trading agents on Pydantic AI by enforcing strict schema validation for NetEase BUFF tools.

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Connect NetEase BUFF MCP to Pydantic AI

Create your Vinkius account to connect NetEase BUFF 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 NetEase BUFF with Pydantic AI

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Validate NetEase BUFF responses in Pydantic AI

Every tool call is checked against your models. If `get_market_items` returns malformed data, your agent stops immediately instead of acting on bad info. You get clean, typed data in every response. This eliminates the risk of hallucinated fields during your trading loops.

Programmatic trading with Pydantic AI

Define your buy criteria as a Pydantic model. The agent compares `get_buy_orders` data against your schema before calling `create_buy_order`. If the data doesn't match your requirements, the agent throws an error. You avoid the silent failures common in standard agent implementations.

Audit NetEase BUFF history with Pydantic AI

Review your performance with structured logs. The agent parses `get_transaction_history` into your models for easy reporting and analysis. Everything is consistent. You can trust that the data moving through your agent is exactly what the server provided, verified at runtime.

Setup guide

Set up NetEase BUFF 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": {
        "netease-buff-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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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 Pydantic AI

Install the pydantic-ai-slim package and initialize an MCPToolset with your endpoint. The agent automatically validates all tool responses.

It does. By enforcing runtime validation, your agent will fail loudly if the API returns unexpected data, preventing costly mistakes.

Yes. You can define a model that represents a 'good' price and have your agent reject any `get_market_items` result that doesn't fit.

The framework supports SSE and Streamable HTTP. Just point your MCPToolset at the Vinkius URL and the agent handles the rest.

The server manages all authentication securely. Your wallet balance and trade history are processed in a zero-trust, isolated environment.

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