How to Use the LLM ROUGE & BLEU Evaluator MCP in CrewAI

Q: How do I add the LLM ROUGE & BLEU Evaluator to my CrewAI agent?

You pass the server URL into the mcps parameter of your agent definition. This makes the calculaterougebleu tool immediately available to that specific agent.

Deploy specialized evaluation agents in CrewAI using precise n-gram scoring for autonomous content verification.

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Connect LLM ROUGE & BLEU Evaluator MCP to CrewAI

Create your Vinkius account to connect LLM ROUGE & BLEU Evaluator to CrewAI 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 CrewAI

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Specialized evaluation agents in CrewAI

Assign a dedicated monitor agent to use the `calculate_rouge_bleu` tool. This agent can watch the output of other crew members and flag any content that deviates from your reference standards. It allows for autonomous quality assurance. Your crew handles the generation, while this agent handles the verification, keeping your operations consistent.

Sequential quality checks in CrewAI

Place the `calculate_rouge_bleu` tool at the end of your CrewAI task sequence. It serves as a final gatekeeper, ensuring every piece of text meets your similarity requirements before it leaves the crew. This approach automates the review process entirely. You don't need manual oversight when the agent can verify the output quality programmatically.

Shared memory evaluation for CrewAI

Use the `calculate_rouge_bleu` tool to compare agent outputs against the shared memory of your crew. It ensures that every agent is aligned with the core reference data stored in your system. It keeps the entire team on the same page. When an agent produces text, the crew uses this tool to verify it fits the established context.

Setup guide

Set up LLM ROUGE & BLEU Evaluator MCP in CrewAI

Prerequisites

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

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke LLM ROUGE & BLEU Evaluator tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="LLM ROUGE & BLEU Evaluator Analyst",
    goal="Access and analyze LLM ROUGE & BLEU Evaluator data via MCP.",
    backstory="Expert analyst with direct LLM ROUGE & BLEU Evaluator access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent LLM ROUGE & BLEU Evaluator transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="LLM ROUGE & BLEU Evaluator Analyst",
        goal="Access and analyze LLM ROUGE & BLEU Evaluator data via MCP.",
        backstory="Expert analyst with direct LLM ROUGE & BLEU Evaluator access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent LLM ROUGE & BLEU Evaluator transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

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Common questions about LLM ROUGE & BLEU Evaluator MCP in CrewAI

You pass the server URL into the `mcps` parameter of your agent definition. This makes the `calculate_rouge_bleu` tool immediately available to that specific agent.

Yes, you can share the tool across your entire crew. Any agent with access can trigger a calculation to verify its own output or that of another agent.

It does. You can assign the tool to a supervisor agent who uses it to review the work submitted by subordinates before deciding if a task is complete.

You provide an endpoint token to the server connection. This keeps your communication secure while the agents perform their evaluations.

The server only holds your text in temporary memory to perform the math. Once the score is returned, the data is wiped to maintain your privacy standards.

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