How to Use the TOML Parser Engine MCP in Google ADK

Q: How does the TOML Parser Engine integrate with the Google ADK?

The parsetoml tool allows your agents to read and transform configuration data before acting on it. Your agent can pass config text, receive the converted JSON or TOML object, and then use that clean data in its workflow.

Q: Does the Google ADK support converting TOML to JSON using the TOML Parser Engine?

Yes. You initiate the conversion by calling parsetoml with 'toml-to-json'. The MCP Server processes it, providing structured data that your agent can easily consume within its enterprise workflow.

Q: If I use the Google ADK, what file types can the TOML Parser Engine parse?

It’s designed for formats like Cargo.toml, pyproject.toml, and wrangler.toml. These are common files in modern cloud deployments that your agents frequently encounter.

Let's give your Google ADK agents reliable config processing with the TOML Parser Engine.

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Connect TOML Parser Engine MCP to Google ADK

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

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Convert configuration files between JSON and TOML.

The `parse_toml` tool converts raw content, allowing your agent to move data between formats. This is critical when bridging information gathered from different systems. Simply pass the text in either TOML or JSON format, and the MCP Server returns the other structure for immediate use by your LlmAgent.

Supports common deployment file types.

It handles structures found in `Cargo.toml`, `pyproject.toml`, and `wrangler.toml`. This gives your enterprise agents the necessary data context from typical cloud infrastructure files. Don't worry about missing syntax; the engine manages nested tables, arrays of tables, and inline details automatically.

Handles complex data structures deterministically.

The server reliably parses complicated inputs like datetime values. It ensures that regardless of how messy the input file is, the output structure remains predictable. This deterministic conversion capability lets your Google ADK agents trust the foundational data they receive before running BigQuery queries or calling Vertex AI.

Setup guide

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

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Common questions about TOML Parser Engine MCP in Google ADK

The `parse_toml` tool allows your agents to read and transform configuration data before acting on it. Your agent can pass config text, receive the converted JSON or TOML object, and then use that clean data in its workflow.

Yes. You initiate the conversion by calling `parse_toml` with 'toml-to-json'. The MCP Server processes it, providing structured data that your agent can easily consume within its enterprise workflow.

It manages nested tables, arrays of tables, and datetime values. This means whether you're dealing with Python or Rust configs, the data structure remains intact after conversion.

It’s designed for formats like `Cargo.toml`, `pyproject.toml`, and `wrangler.toml`. These are common files in modern cloud deployments that your agents frequently encounter.

The engine is deterministic. It guarantees predictable conversions, so when your agent uses the output of `parse_toml`, it knows exactly what structure to expect every time.

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