How to Use the Lingyi Wanwu MCP in Google ADK

Q: How do I configure the Lingyi Wanwu MCP Server with Google ADK?

Instantiate McpToolset using the streamable HTTP server parameters pointing to your Vinkius endpoint. Pass this toolset into your LlmAgent constructor to expose the tools to Gemini.

Q: Can I restrict which Lingyi Wanwu tools my Google ADK agent can access?

Yes. Use the optional toolnames filter when initializing the toolset in Google ADK. This lets you limit the agent to specific tools like chatcompletions while hiding administrative tools.

Q: How do Lingyi Wanwu embeddings integrate with Google ADK vector workflows?

You call getembeddings through the MCP Server to generate vectors that align with the Yi model semantic space. These vectors can be stored directly in Vertex AI Vector Search or BigQuery for low-latency retrieval.

Connect Lingyi Wanwu models to your Google ADK pipelines to process massive datasets alongside Google Cloud data.

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Connect Lingyi Wanwu MCP to Google ADK

Create your Vinkius account to connect Lingyi Wanwu to Google ADK 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 Lingyi Wanwu with Google ADK

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Execute Yi model queries from Google ADK

`chat_completions` sends text prompts directly to Yi models, allowing your Google ADK agent to mix Gemini reasoning with specialized regional models. Here's the thing: this MCP Server lets you handle cross-border localization without leaving your Google Cloud environment. The server exposes `list_models` so your enterprise agent can dynamically query available endpoints. This ensures your code doesn't break when model versions change on the provider side.

Filter BigQuery inputs before model execution

`check_moderation` scans incoming database records from BigQuery before they reach the model via this MCP setup. You run this tool inside your Google ADK pipeline to filter out sensitive or violating content before it gets processed by the Yi endpoints. This step is critical for maintaining compliance when processing user-generated content at scale. It keeps your pipeline clean and prevents your agent from generating unsafe outputs.

Generate embeddings for enterprise search

`get_embeddings` converts text chunks from your Google Cloud storage buckets into vector representations. Your Google ADK agent can then load these vectors to perform high-accuracy semantic search. To keep cloud costs in check, `get_usage` monitors your API consumption directly. The agent can check this tool to track token usage against your project's resource quotas in real time.

Setup guide

Set up Lingyi Wanwu MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Lingyi Wanwu tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Lingyi Wanwu_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Lingyi Wanwu tools via MCP.",
    tools=mcp_tools,
)

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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 Lingyi Wanwu. 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 Lingyi Wanwu MCP in Google ADK

Instantiate `McpToolset` using the streamable HTTP server parameters pointing to your Vinkius endpoint. Pass this toolset into your `LlmAgent` constructor to expose the tools to Gemini.

Yes. Use the optional `tool_names` filter when initializing the toolset in Google ADK. This lets you limit the agent to specific tools like `chat_completions` while hiding administrative tools.

You call `get_embeddings` through the MCP Server to generate vectors that align with the Yi model semantic space. These vectors can be stored directly in Vertex AI Vector Search or BigQuery for low-latency retrieval.

Yes. Google ADK supports both transport methods. For managed Vinkius deployments, you will use the HTTP transport with your secure endpoint token.

All moderation check payloads are processed in memory within secure, sandboxed environments. Vinkius does not log or retain the text content analyzed by `check_moderation`, keeping your corporate data isolated.

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