How to Use the Duolingo MCP in CrewAI

Q: Can I filter which Duolingo tools my CrewAI agents use?

Use the toolfilter argument in your agent setup. This lets you restrict access to specific tools like getuserbyusername while blocking access to others.

Deploy autonomous research crews to track Duolingo metrics using the Duolingo MCP Server.

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Connect Duolingo MCP to CrewAI

Create your Vinkius account to connect Duolingo 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 Duolingo with CrewAI

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Perplexity

Autonomous profile research crews

Assign a research agent to fetch data via `get_user_by_id`. The crew collects progress metrics while a separate analysis agent evaluates the performance trends. This specialization allows one agent to focus on raw data collection while another interprets the numbers. They work in tandem to give you a clear picture of your learning habits.

Collaborative leaderboard monitoring

Run a crew where one agent manages `get_daily_leaderboard` and another agent acts on the findings. If your rank drops, the moderator agent takes over to update your goals. This setup removes the need for manual intervention. The agents share memory of the session, so they don't repeat work and maintain a consistent view of your status.

Automated store inventory tracking

Use a specialized agent to watch shop items with `get_store_items`. When new items appear, the agent logs them for the rest of the crew to review. This keeps your data current without you checking the app manually. The agents handle the repetitive tasks, freeing you to focus on actual language practice.

Setup guide

Set up Duolingo 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 Duolingo tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Duolingo 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="Duolingo Analyst",
        goal="Access and analyze Duolingo data via MCP.",
        backstory="Expert analyst with direct Duolingo access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Duolingo 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 Duolingo MCP in CrewAI

Include the server URL in your agent definition. CrewAI will automatically load the tools and make them available to your research and analysis agents.

Yes. By using shared memory within your crew, one agent can fetch the leaderboard with `get_leaderboard` and pass the results to another agent for summary.

You can set up a monitor agent that checks your streak daily. If it finds a decline, it can signal your other agents to take corrective action.

The server operates within a sandboxed environment. Your profile details, including your current language and XP, are processed only in memory and are never shared outside the agent's task scope.

Use the tool_filter argument in your agent setup. This lets you restrict access to specific tools like `get_user_by_username` while blocking access to others.

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