How to Use the Regex High-Perf Parser MCP in Google ADK

Structured Data Extraction for Google ADK Enterprise Agents.

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Connect Regex High-Perf Parser MCP to Google ADK

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Setup Regex High-Perf Parser with Google ADK

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Connect to BigQuery Data

Your agents are already running on Google Cloud and need data from BigQuery. This MCP lets your agent parse massive text inputs before acting, ensuring that structured information is extracted reliably into the workflow. It's a solid fit for pre-processing raw audit logs or unstructured documents pulled into your GCP environment.

Reliable Parsing in Long Context

When dealing with 1M+ tokens of context, you can't afford parsing failures. This tool guarantees that even within huge document dumps, the extraction is mathematically certain using pure regex. This deterministic approach keeps your long-context reasoning stable and accurate, preventing hallucination from skewing your enterprise logic.

Structured Data for Vertex AI Pipelines

If you're building multi-step pipelines in Vertex AI, the input data needs to be rock solid. This MCP provides a definitive way to pull out specific entities from text into an array format. It acts as a reliable pre-processing step that ensures subsequent nodes in your pipeline receive clean, validated inputs.

Setup guide

Set up Regex High-Perf Parser 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 Regex High-Perf Parser 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="Regex High-Perf Parser_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Regex High-Perf Parser tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Regex High-Perf Parser MCP in Google ADK

The parser provides guaranteed extraction of structured data. It runs pure regex on text blocks to produce an exact array of matches, preventing the agent from hallucinating or misinterpreting raw inputs.

Yes. You can use this MCP to parse unstructured text pulled into your GCP workspace, preparing it for analysis against BigQuery datasets. It handles the extraction before the database query.

It can. Because it relies on deterministic pattern matching, it ignores common LLM weaknesses around ambiguous delimiters and spacing, making it robust for varied real-world data.

Use this MCP when you need to reliably pull structured entities from huge text inputs—for instance, extracting user IDs or product codes from large log files before analysis.

It touches unstructured and semi-structured text. By converting it into a clean array of strings, it makes that information instantly usable for downstream BigQuery queries or Vertex AI model inputs.

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