How to Use the Honeybadger (Error Tracking) MCP in Google ADK

Q: How do I restrict which Honeybadger (Error Tracking) tools are exposed to my Google ADK agent?

You use the toolnames filter when initializing the McpToolset. This lets you expose read-only tools like listfaults while completely hiding write operations from the MCP server.

Q: How does Gemini use Honeybadger (Error Tracking) data in a Google ADK workflow?

Gemini pulls deep backtrace data using getnotice. Thanks to its massive context window, the model can ingest the entire raw JSON payload to find subtle code bugs that shorter-context models truncate.

Q: Does this server support uptime monitoring updates?

You can check the status of your targets using listsites. The server currently focuses on reading site configurations and error states rather than modifying your uptime targets.

Connect the Google ADK to Honeybadger to analyze production exceptions using long-context Gemini models and BigQuery data.

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Connect Honeybadger (Error Tracking) MCP to Google ADK

Create your Vinkius account to connect Honeybadger (Error Tracking) 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 Honeybadger (Error Tracking) with Google ADK

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Analyze massive error volumes with Google ADK

The `list_faults` tool returns class names, error messages, environments, and occurrence counts for active exceptions in a project. Your agent feeds these lists directly into Gemini's million-token context window to identify recurring patterns over long periods. You can cross-reference these fault patterns against your historical application logs stored in BigQuery. This setup allows your Google ADK agent to find silent regressions that standard alerting filters often miss.

Audit team access and monitoring configurations

The `list_members` and `list_sites` tools retrieve your project's active team roster and uptime monitoring targets. Your agent runs these tools to verify that your on-call alerts align with your team directory and that critical endpoints are actively monitored. If a site goes down or a member leaves, your agent detects the discrepancy. It can flag unmonitored routes or stale permissions without requiring a manual audit of your dashboard settings.

Deep dive into specific exception details

The `get_project` and `get_fault` tools load the full metadata and configuration details of a targeted error group. Your agent uses these details to map which specific microservices are throwing the highest volume of exceptions. Because the Google ADK integrates natively with Vertex AI, you can run predictive analysis on these error trends. The agent spots correlation between specific project tokens and recurring infrastructure bottlenecks before they cause downtime.

Setup guide

Set up Honeybadger (Error Tracking) 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 Honeybadger (Error Tracking) 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="Honeybadger (Error Tracking)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Honeybadger (Error Tracking) 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 Honeybadger. 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 Honeybadger (Error Tracking) MCP in Google ADK

You use the tool_names filter when initializing the McpToolset. This lets you expose read-only tools like `list_faults` while completely hiding write operations from the MCP server.

Yes. The server runs in a secure Vinkius sandbox, but your Google ADK agent connects to the MCP server via standard HTTP transport from your GKE cluster or Cloud Run instance.

Gemini pulls deep backtrace data using `get_notice`. Thanks to its massive context window, the model can ingest the entire raw JSON payload to find subtle code bugs that shorter-context models truncate.

You can check the status of your targets using `list_sites`. The server currently focuses on reading site configurations and error states rather than modifying your uptime targets.

All authentication tokens and team rosters are handled in memory within an isolated V8 sandbox running the MCP server. Vinkius ensures that these sensitive credentials are never written to disk or exposed to external LLM logging systems.

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