How to Use the DocBreach MCP in Google ADK

Q: How does DocBreach handle authentication specs within the Google ADK?

docs.read automatically extracts a 'Related Documentation Links' section, which your Google ADK agent uses to find authentication and getting-started pages. This ensures the model checks auth requirements before writing any integration code.

Feed clean API schemas directly to your Google ADK agents to automate enterprise data pipelines.

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Connect DocBreach MCP to Google ADK

Create your Vinkius account to connect DocBreach 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 DocBreach with Google ADK

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Feed Clean Specs to Gemini via Google ADK

`docs.extract` parses OpenAPI, Swagger, and Postman specs to feed clean endpoint schemas directly to your Gemini models. This tool bypasses slow browser automation, letting your Google ADK agents generate accurate API payloads without hallucinating fields. Because Gemini handles massive token contexts, you can extract large schemas and filter them by tags. Your agent processes these structured schemas alongside BigQuery datasets to automate complex cloud workflows.

Map Enterprise Docs for Long-Context Reasoning

`docs.map` indexes the entire structure of any documentation domain to build a clean table of contents. Your Google ADK agent parses this map to understand where authentication guides and endpoint references reside before reading them. Once mapped, the agent uses `docs.read` to fetch specific pages as clean markdown. This targeted reading prevents your Vertex AI runs from getting bogged down by raw HTML boilerplate or irrelevant navigation links.

Targeted Search Inside Your Cloud Environment

`docs.search` runs scoped queries within a specific documentation domain to find exact code patterns or error definitions. Your agent uses this tool to locate specific API headers without wasting compute cycles on broad web searches. When your agent needs to find a new service, `docs.discover` uses descriptive queries to locate the correct documentation homepages. You can expose these tools through the MCP Server transport configuration, letting your enterprise agents fetch live API data securely.

Setup guide

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

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Common questions about DocBreach MCP in Google ADK

`docs.read` automatically extracts a 'Related Documentation Links' section, which your Google ADK agent uses to find authentication and getting-started pages. This ensures the model checks auth requirements before writing any integration code.

Yes, you can use the optional tool names filter in your McpToolset configuration to expose only specific tools. For example, you might restrict an agent to `docs.search` and `docs.read` while keeping discovery tools disabled.

No, this MCP server runs in a secure, zero-trust sandbox that does not require GCP credentials or external API keys. You connect using the single Vinkius endpoint token inside your StreamableHttpServerParameters.

The tool parses HTML, JSON, YAML, OpenAPI specs, Postman Collections, and PDFs under 5MB. It outputs clean markdown, which is ideal for the long-context reasoning capabilities of Gemini models.

All documentation URLs and parsed text are processed in memory within an ephemeral sandbox that shuts down immediately after execution. Your requests never touch persistent storage, ensuring that proprietary API structures and internal documentation targets remain confidential.

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