How to Use the OpenAPI Validator Engine MCP in Google ADK

Q: How do I integrate the OpenAPI Validator Engine with Google ADK for my cloud agents?

Register the server using the McpToolset class in your agent setup. This makes the validateopenapi tool available to your LLM automatically.

Validate your API contracts within Google ADK. Keep your Google Cloud agents running on clean, compliant specifications.

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Connect OpenAPI Validator Engine MCP to Google ADK

Create your Vinkius account to connect OpenAPI Validator Engine to Google ADK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup OpenAPI Validator Engine with Google ADK

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Google ADK spec validation integration

Use `validate_openapi` as a gatekeeper for your agent's API interactions. It verifies your spec integrity before the agent attempts to map data to your BigQuery or Vertex AI resources. This catches structural issues early. You stop the agent from proceeding if the API definition doesn't align with the expected schema, preventing logic crashes.

Enforce contract stability in Google ADK

Your agents can now check their own tool definitions. By running `validate_openapi`, you ensure that the OpenAPI or Swagger files driving your agent's actions are strictly compliant. It removes the guesswork from agent-driven API development. If an error exists, the agent receives a clear report that allows it to flag the issue before you deploy to your cloud infrastructure.

Multi-version support for Google ADK agents

The engine processes everything from Swagger 2.0 to OpenAPI 3.2 without extra configuration. This gives your Google ADK agents the flexibility to work with diverse API versions across your enterprise. It handles the validation logic locally. You get a consistent response format regardless of whether the input is a legacy Swagger file or a modern OpenAPI spec.

Setup guide

Set up OpenAPI Validator Engine 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 OpenAPI Validator Engine 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="OpenAPI Validator Engine_agent",
    model="gemini-2.0-flash",
    instruction="You have access to OpenAPI Validator Engine tools via MCP.",
    tools=mcp_tools,
)

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Common questions about OpenAPI Validator Engine MCP in Google ADK

Register the server using the `McpToolset` class in your agent setup. This makes the `validate_openapi` tool available to your LLM automatically.

The engine handles specs up to standard enterprise sizes efficiently. It parses your OpenAPI 3.1 definitions and returns structural errors immediately.

Yes. The validator is designed to support Swagger 2.0 alongside all newer OpenAPI versions. It ensures your legacy cloud API specs are error-free.

Every error returned includes the exact path in your spec where the violation occurred. This helps your agent identify and report exactly what needs adjustment.

The server treats your spec as ephemeral, transient input. No data is stored, and the validation happens entirely within the scope of your specific agent call.

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