How to Use the YAML Parser Engine MCP in Google ADK

Connect your enterprise workflows with Google ADK using the YAML Parser Engine.

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Connect YAML Parser Engine MCP to Google ADK

Create your Vinkius account to connect YAML Parser Engine 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 YAML Parser Engine with Google ADK

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Process Config Data with YAML Parser Engine

The `parse_yaml` tool lets your agent convert data between JSON and YAML. Since you're building enterprise agents on Google Cloud, this reliable conversion is key when interacting with services like BigQuery. It handles complex structures, including anchors and aliases. You can pass it content and specify if you need 'yaml-to-json' or 'json-to-yaml'.

MCP Server Reliability for Google ADK

The MCP Server is designed to run within your existing Google Cloud infrastructure. It handles data conversion needed by agents that interact with specialized services like Vertex AI. Because the tool uses an industry-standard YAML package, it supports both YAML 1.1 and 1.2 standards while preserving comments.

Type Conversion for Google ADK

The `parse_yaml` function is straightforward: you give it content and a direction ('yaml-to-json' or 'json-to-yaml'). This simple mechanism allows your Long-context Gemini agents to reliably ingest structured config data. This capability ensures that the massive context window (1M+ tokens) can work with accurate, parsed configuration details.

Setup guide

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

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Common questions about YAML Parser Engine MCP in Google ADK

You expose the MCP Server as a toolset to your LlmAgent. When the agent needs config parsing, it calls `parse_yaml`. This keeps structured data handling external and clean.

Yes. The server explicitly supports multi-document structures when converting between JSON and YAML, ensuring complex configuration files don't break your agent logic.

The underlying engine is highly robust. It passes the official YAML test suite, giving you confidence that the data passed to your agents is accurate and correctly formatted.

It processes string inputs containing structured configuration—specifically YAML or JSON. It converts this content into the target format you specify, maintaining structural integrity.

The server touches configuration data (YAML/JSON strings). Since your agents are running on Google Cloud, standard GCP security practices apply to the endpoint token management.

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