How to Use the Cerebras Inference MCP in Google ADK

Q: Can I restrict Cerebras Inference tool usage in Google ADK?

Use the toolnames filter in your McpToolset definition. This hides specific tools from the agent, keeping your implementation focused on only what you need.

Connect Cerebras Inference to Google ADK to run high-speed inference alongside your BigQuery data pipelines.

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Connect Cerebras Inference MCP to Google ADK

Create your Vinkius account to connect Cerebras Inference 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 Cerebras Inference with Google ADK

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Cerebras Inference toolset for Google ADK

Integrate the full suite of generation tools like `create_chat_completion` into your `LlmAgent` definition. Your agent gains access to specialized hardware for inference. `list_public_models` lets you query current hardware capability without exposing your API key. You maintain strict control over your agent's access level.

Automated batching in Google ADK

Use `create_batch` to process large datasets stored in your cloud environment. The MCP server handles the heavy lifting while your agent coordinates the next steps. `list_batches` provides a summary of all active jobs. This allows your Google ADK agent to report status back to your internal dashboards.

Managing files via Google ADK

Upload datasets for model processing using `upload_file`. Your agent can then inspect file metadata with `get_file` to confirm the batch is ready. `delete_file` cleans up after the job finishes. You keep your Google Cloud footprint small by removing files after the inference task concludes.

Setup guide

Set up Cerebras Inference 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 Cerebras Inference 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="Cerebras Inference_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Cerebras Inference tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Cerebras Inference MCP in Google ADK

Pass the server URL to the McpToolset object in your agent setup. This maps the server tools directly into the agent's tool list for immediate use.

It does, by letting the agent call the batch tools asynchronously. You don't need to wait for the inference to finish before moving to the next task.

Use the tool_names filter in your McpToolset definition. This hides specific tools from the agent, keeping your implementation focused on only what you need.

All files you upload are isolated in a V8 sandbox managed by Vinkius. Access is restricted to your authenticated session, and data is purged after use.

The server only touches the files you explicitly upload for inference. It has no direct connection to your BigQuery instance unless you pipe that data through the agent.

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