How to Use the LLM ROUGE & BLEU Evaluator MCP in Google ADK

Q: How do I register the LLM ROUGE & BLEU Evaluator in Google ADK?

Initialize McpToolset with your Vinkius HTTP server parameters. Then, pass that toolset into your LlmAgent definition to let Gemini access the evaluation functions.

Q: How does this n-gram tool compare to using Vertex AI evaluation tools?

Vertex AI tools are great for semantic evaluation, but the calculaterougebleu tool gives you instant, zero-cost mathematical similarity scores without setting up complex ML pipelines.

Score Gemini's long-context outputs against BigQuery reference datasets directly within your Google ADK agent workflows.

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Connect LLM ROUGE & BLEU Evaluator MCP to Google ADK

Create your Vinkius account to connect LLM ROUGE & BLEU Evaluator 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 LLM ROUGE & BLEU Evaluator with Google ADK

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Match Gemini outputs against BigQuery records

The `calculate_rouge_bleu` tool lets your Google ADK agent compare generated summaries with ground-truth data pulled from BigQuery. This ensures your enterprise models are not hallucinating facts during long-context analysis. Your agent runs the calculation immediately after generating text. Because this MCP Server integrates directly into your pipeline, you can block inaccurate reports from saving back to your cloud database.

Validate massive context windows with Google ADK

When Gemini processes a million tokens, keeping track of output accuracy is notoriously difficult. The `calculate_rouge_bleu` tool solves this by mathematically scoring the generated text against your reference documents. You pass the toolset to your `LlmAgent` to automate this check. It acts as a deterministic filter, flagging whenever a long-form generation deviates too far from your source material.

Restrict tools to optimize Gemini reasoning

Configuring this MCP Server with the `tool_names` filter ensures your Google ADK agent only accesses the exact evaluation tools it needs. This keeps the model's action space small and focused. Your agent calls `calculate_rouge_bleu` without getting distracted by unrelated capabilities. This targeted approach preserves your token budget and keeps reasoning speeds high.

Setup guide

Set up LLM ROUGE & BLEU Evaluator 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 LLM ROUGE & BLEU Evaluator 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="LLM ROUGE & BLEU Evaluator_agent",
    model="gemini-2.0-flash",
    instruction="You have access to LLM ROUGE & BLEU Evaluator tools via MCP.",
    tools=mcp_tools,
)

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Common questions about LLM ROUGE & BLEU Evaluator MCP in Google ADK

Initialize `McpToolset` with your Vinkius HTTP server parameters. Then, pass that toolset into your `LlmAgent` definition to let Gemini access the evaluation functions.

Absolutely. Your agent can pull source text from BigQuery, generate a summary, and then use `calculate_rouge_bleu` to score the summary's overlap with the source.

Yes, it fully supports both Stdio and HTTP transports. You can configure the connection parameters to match your specific Google Cloud infrastructure requirements.

Vertex AI tools are great for semantic evaluation, but the `calculate_rouge_bleu` tool gives you instant, zero-cost mathematical similarity scores without setting up complex ML pipelines.

Your candidate and reference text strings never leave the secure, ephemeral Vinkius sandbox. The server runs your calculations in isolation and discards the text data immediately after returning the scores.

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