How to Use the Datadog AI (LLM Observability) MCP in Google ADK

Q: How do I integrate Datadog AI (LLM Observability) with Google ADK?

You use the McpToolset class by passing your server URL. This connects your agent to the Datadog ecosystem, allowing you to use tool names to filter which capabilities your agent accesses.

Connect Google ADK agents to your Datadog performance logs for deep enterprise visibility.

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Connect Datadog AI (LLM Observability) MCP to Google ADK

Create your Vinkius account to connect Datadog AI (LLM Observability) 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 Datadog AI (LLM Observability) with Google ADK

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Query metrics with Google ADK

Run `query_metrics` to grab specific token counts from `datadog.llm_observability.tokens` within your Vertex AI workflows. This keeps your enterprise agents aligned with resource limits. It works by pulling raw stats directly into your agent's reasoning loop. You stop guessing about model performance and start acting on verified numbers.

Manage incidents with Google ADK

Use `list_incidents` to dispatch validation checks that route through your gateway history. It identifies active arrays spanning your native authentication logs. This provides a clear view of where your agent encountered obstacles. You can then refine your prompts to avoid those specific failure states.

Automate monitoring for Google ADK

Trigger `create_monitor` to vaporize explicit validations that extract churn flags from your logs. It keeps your monitoring setup clean and focused on actual service health. This tool removes the noise from your dashboard. It ensures your agents only alert you when there is a real issue to address.

Setup guide

Set up Datadog AI (LLM Observability) 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 Datadog AI (LLM Observability) 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="Datadog AI (LLM Observability)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Datadog AI (LLM Observability) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Datadog AI (LLM Observability) MCP in Google ADK

You use the `McpToolset` class by passing your server URL. This connects your agent to the Datadog ecosystem, allowing you to use tool names to filter which capabilities your agent accesses.

It is. You can trace interactions involving million-token contexts by querying the span tool to verify what the agent actually saw.

The tools allow your agent to enumerate its own performance metrics. It can check its own billing and usage data to adjust its behavior dynamically.

You provide an endpoint token via the Vinkius managed environment. All requests are handled over standard, encrypted HTTP or Stdio transports.

Your BigQuery and Vertex AI data never leaves your infrastructure unless you explicitly send it to the observability platform. The server acts as a scoped interface that only touches the specific metrics you query.

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