How to Use the Amazon Bedrock KB MCP in Google ADK

Q: What is the best way to list Amazon Bedrock KB indexes in Google ADK?

Call listknowledgebases during your agent's setup phase. This populates your agent's toolset with the available indices it can interact with.

Link your Google ADK enterprise agents to Amazon Bedrock KB for high-volume context processing.

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Connect Amazon Bedrock KB MCP to Google ADK

Create your Vinkius account to connect Amazon Bedrock KB 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 Amazon Bedrock KB with Google ADK

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Scale context with Google ADK

Connect your Gemini-powered agents to your knowledge base using `get_knowledge_base`. This allows your agent to pull context into its massive 1M+ token window. It works well for enterprise tasks where you need to compare data across multiple sources. You get the benefits of two ecosystems in one workflow.

Map data sources via Google ADK

Run `list_data_sources` to see exactly what indices your agent can query. This mapping is vital when you are building complex agents that need to cross-reference BigQuery data with document stores. It gives your agent a clear inventory of available information. You avoid hitting empty or misconfigured indices.

Run grounded agents in Google ADK

Use `retrieve` to fetch specific vector matches for your agent to process. By keeping the logic inside your Google Cloud environment, you maintain a consistent architecture. It keeps your data retrieval fast and predictable. Your agent stays focused on the reasoning task rather than the infrastructure.

Setup guide

Set up Amazon Bedrock KB 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 Amazon Bedrock KB 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="Amazon Bedrock KB_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Amazon Bedrock KB 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 Amazon Bedrock. 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 Amazon Bedrock KB MCP in Google ADK

Yes, this MCP server provides a standard interface for your Google ADK agent to query AWS resources. It bridges the gap between your cloud services.

You should use the Stdio transport for local development or keep your server close to your agent deployment. This reduces the time spent on network round-trips.

Absolutely. Your agent can pull large chunks of data via the server and feed them into the Gemini context window for deep analysis.

Call `list_knowledge_bases` during your agent's setup phase. This populates your agent's toolset with the available indices it can interact with.

The server uses TLS for all transit between your agent and the AWS environment. Your vector data remains encrypted at rest within your AWS account and is never cached by the server.

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