How to Use the LLM ROUGE & BLEU Evaluator MCP in OpenAI Agents SDK

Q: How do I set up the LLM ROUGE & BLEU Evaluator with my OpenAI Agents SDK?

Install the package, then configure MCPServerStreamableHttp pointing to your Vinkius endpoint. Pass the server instance inside the mcpservers list when initializing your Agent.

Run deterministic n-gram scoring directly inside your OpenAI Agents SDK pipelines to catch hallucinated outputs before they hit production.

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Connect LLM ROUGE & BLEU Evaluator MCP to OpenAI Agents SDK

Create your Vinkius account to connect LLM ROUGE & BLEU Evaluator to OpenAI Agents SDK 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 OpenAI Agents SDK

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Stop bad text in OpenAI Agents SDK pipelines

The `calculate_rouge_bleu` tool gives your agent the math it needs to verify its own text generation against gold-standard reference documents. Instead of guessing if a model output is accurate, the agent calls this tool to get immediate decimal scores for n-gram overlap. This setup fits inside your OpenAI Agents SDK runtime, allowing you to establish strict quality thresholds. If a generated summary scores below your target ROUGE mark, the agent catches it instantly and runs a correction loop before sending the text to the user.

Trace evaluation metrics in the OpenAI dashboard

Integrating this MCP Server into your multi-agent architecture lets you monitor text quality across complex handoffs. When your specialized agents swap tasks, they run `calculate_rouge_bleu` to ensure the core message remains intact. You get to see every single score calculation logged directly inside your OpenAI tracing dashboard. This means you do not have to build custom logging infrastructure just to track how your model's accuracy shifts over time.

Real-time scoring without expensive LLM API calls

Using the `calculate_rouge_bleu` tool avoids the latency and cost of calling another model just to grade your primary agent. This tool executes local string math to return BLEU and ROUGE metrics in milliseconds. By configuring the MCP Server with `cacheToolsList=True`, your OpenAI Agents SDK client loads the tool definition once. This keeps your pipeline fast and ensures your production guardrails do not slow down the user experience.

Setup guide

Set up LLM ROUGE & BLEU Evaluator MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all LLM ROUGE & BLEU Evaluator tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives LLM ROUGE & BLEU Evaluator tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate LLM ROUGE & BLEU Evaluator tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="LLM ROUGE & BLEU Evaluator Agent",
            instructions="You have access to LLM ROUGE & BLEU Evaluator tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about LLM ROUGE & BLEU Evaluator MCP in OpenAI Agents SDK

Install the package, then configure `MCPServerStreamableHttp` pointing to your Vinkius endpoint. Pass the server instance inside the `mcp_servers` list when initializing your Agent.

Yes. You can compare new agent outputs against a static dataset of correct answers using the `calculate_rouge_bleu` tool. This lets you catch prompt regressions instantly.

When one agent hands off a task, the supervisor agent can run `calculate_rouge_bleu` on the output to ensure the draft text matches the original instructions before finalizing the run.

It costs nothing and runs in milliseconds. While LLM judges are slow and expensive, the `calculate_rouge_bleu` tool uses deterministic math to give you instant, reproducible metrics.

The candidate and reference text strings are processed in an isolated, ephemeral V8 sandbox. No data is stored, logged, or used for training, ensuring your raw text inputs remain completely private.

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