How to Use the Cohere (Embed & Rerank) MCP in Google ADK

Q: How does Cohere (Embed & Rerank) integrate with Google ADK?

You register the toolset in your LlmAgent initialization. It supports standard transport methods, allowing your agents to call the tools alongside your BigQuery queries.

Connect Cohere (Embed & Rerank) to your Google ADK agents to process 1M+ token contexts with high-accuracy reranking.

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Connect Cohere (Embed & Rerank) MCP to Google ADK

Create your Vinkius account to connect Cohere (Embed & Rerank) 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 Cohere (Embed & Rerank) with Google ADK

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Scale Google ADK with Cohere classification

Run `classify_texts` to map incoming user prompts to specific categories defined in your system. This allows your agent to route tasks to specialized BigQuery workflows without manual parsing. Your agent receives the class labels as structured strings. It keeps your logic clean by letting the model handle the classification heavy lifting.

Optimize long-context reasoning in Google ADK

Use `rerank_documents` to feed the Gemini context window only the most critical information. By pruning irrelevant data, your agent stays focused on the actual user intent. This keeps your token usage efficient. You avoid paying for irrelevant context while maintaining high retrieval performance.

Automate text transformation in Google ADK

The `chat_completion` tool performs explicit transformations on your text assets before they reach the LLM. You define the logic, and the server executes the formatting steps. This maintains consistent output across all your Google Cloud services. Every response follows the structure you demand for your enterprise applications.

Setup guide

Set up Cohere (Embed & Rerank) 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 Cohere (Embed & Rerank) 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="Cohere (Embed & Rerank)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Cohere (Embed & Rerank) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Cohere (Embed & Rerank) MCP in Google ADK

You register the toolset in your `LlmAgent` initialization. It supports standard transport methods, allowing your agents to call the tools alongside your BigQuery queries.

Yes, you can pass large document lists to the reranker. It returns ranked arrays that help your agents select the right data from your cloud storage.

You can restrict the exposed tools easily. If your agent only needs the embedding functionality, you can filter out the rest in your configuration.

The MCP server runs as a sidecar. Your agent connects to it via HTTP, keeping your main agent code isolated from the tool implementation.

Your data is treated as ephemeral noise. The server handles raw string inputs for tokenization or reranking, but the service deletes all payloads immediately after the request finishes.

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