How to Use the Fuzzy String Distance Engine MCP in Google ADK

Let your Google ADK agents clean and link data directly from BigQuery with fast, local fuzzy string matching.

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Connect Fuzzy String Distance Engine MCP to Google ADK

Create your Vinkius account to connect Fuzzy String Distance 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 Fuzzy String Distance Engine with Google ADK

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Fuzzy Match BigQuery Data

The `calculate_fuzzy_distance` tool lets your Gemini-powered agent operate directly on your cloud data. The agent can pull two columns of messy, user-entered names from a BigQuery table and get to work. By calling the tool, the agent can score the similarity between records to find duplicates or create links. This happens on command, allowing you to build sophisticated data cleaning routines that run right where your data lives.

Local Logic, Cloud Scale

The actual string comparison is handled by the MCP server, which is fast and self-contained. Your Google ADK agent acts as the orchestrator, pulling data from Google Cloud, sending it to the tool for a quick calculation, and then using the result. This architecture gives you the best of both worlds. You get the massive scale and data access of Google Cloud, combined with the speed and privacy of a dedicated, single-purpose tool for the core logic.

Build Data Pipelines with this MCP Server

Don't just think of this as a conversational tool. Use the Google ADK to build an automated data processing pipeline. A Cloud Function trigger can invoke your agent on a schedule or when new data arrives in a bucket. The agent, equipped with the `calculate_fuzzy_distance` tool, can then automatically deduplicate the new records and write the cleaned data back to another BigQuery table. It's an effective way to automate data hygiene.

Setup guide

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

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Common questions about Fuzzy String Distance Engine MCP in Google ADK

Your agent, built with the Google ADK, acts as the bridge. It queries data from BigQuery, passes strings to the `calculate_fuzzy_distance` tool from this MCP server, and then uses the resulting score to decide its next action, like writing linked IDs back to BigQuery.

Yes. The agent's LLM, whether it's Gemini or another model, decides when to call the tool. The tool itself is model-agnostic; it just performs the calculation the agent requests.

It depends on the data. Use 'Jaro-Winkler' for matching short identifiers like customer or product names. For comparing blocks of text, like product descriptions, 'Dice's Coefficient' is a better choice.

The server itself is very fast for individual calculations. For large datasets, the bottleneck will be the agent's logic of iterating and calling the tool. For millions of records, you should have the agent process them in batches.

The text strings you send for comparison are processed in memory within a zero-trust sandbox and are immediately discarded. The Vinkius platform ensures no persistence of your company's data on the MCP server.

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