Do I need a Duolingo account to use this?

No! Public endpoints like user profiles, version info and dictionary hints work without authentication. Just provide a username to look up. For private data (your own progress, store items), you'd need to be logged into Duolingo.

What languages are supported?

Duolingo supports 40+ languages including English, Spanish, French, German, Italian, Portuguese, Japanese, Korean, Chinese, Hindi and many more. Use get_version_info to see the full list of supported language codes.

Can I get my full learning progress?

The public API returns limited profile data. For full progress including individual skill levels and detailed stats, you need to be authenticated with your Duolingo session. The username-based endpoint returns basic info like total XP, streak and current language.

Can I get translation hints for vocabulary?

Yes! Use get_dictionary_hints with target and source language codes plus a list of words. It returns common translations for each word, useful for building flashcards or studying vocabulary.

Duolingo MCP Server for CrewAI 8 tools — connect in under 2 minutes

Name: Duolingo
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 8 Tools Framework

Connect your CrewAI agents to Duolingo through Vinkius, pass the Edge URL in the `mcps` parameter and every Duolingo tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Duolingo Specialist",
    goal="Help users interact with Duolingo effectively",
    backstory=(
        "You are an expert at leveraging Duolingo tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Duolingo "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 8 available tools "
        "and what they can do."
    ),
)

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

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Duolingo MCP Server

Connect to Duolingo and explore language learning progress through natural conversation — no API key needed for public data.

When paired with CrewAI, Duolingo becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Duolingo tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

User Profiles — View any user's public profile including XP, streak, language and skill progress
Version Info — Get API version info including supported languages and features
Dictionary Hints — Get translation hints between any two supported languages
Store Items — Browse Duolingo store items like streak freezes and power-ups
Leaderboards — View leaderboard rankings by XP for users and languages
Friends — See a user's friends list with their streaks and XP

The Duolingo MCP Server exposes 8 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Duolingo to CrewAI via MCP

Follow these steps to integrate the Duolingo MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 8 tools from Duolingo

Why Use CrewAI with the Duolingo MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Duolingo through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Duolingo + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Duolingo MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Duolingo for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Duolingo, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Duolingo tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Duolingo against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Duolingo MCP Tools for CrewAI (8)

These 8 tools become available when you connect Duolingo to CrewAI via MCP:

get_daily_leaderboard

Shows top users by XP for the current day. Optionally specify language code and timezone. Get daily leaderboard for a language

get_dictionary_hints

Returns translation pairs for the given tokens. Useful for building flashcards or vocabulary tools. Target and source are language codes (e.g. "es", "fr", "de", "en"). Get dictionary hints for word translations

get_friends

Optionally provide a user ID. Returns friend usernames, IDs, streaks and XP totals. Get Duolingo friends list

get_leaderboard

Optionally provide a user ID to get leaderboard info for a specific user. Returns rankings with usernames, XP totals and positions. Get Duolingo leaderboard data

get_store_items

Requires authentication for full details. Returns item IDs, names, prices and descriptions. Get Duolingo store items

get_user_by_id

Returns the same data as get_user_by_username but uses the numeric ID instead. Get a Duolingo user profile by user ID

get_user_by_username

Returns username, user ID, avatar, current language, total XP, streak, skills and progress. Note: Full profile data (detailed progress, skill trees) requires authentication and only works for your own account. Get a Duolingo user profile by username

get_version_info

Useful for understanding available language codes and API capabilities. Get Duolingo API version information

Example Prompts for Duolingo in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Duolingo immediately.

"Show me the Duolingo profile for user 'john_doe'."

"Get translation hints for 'hello', 'world', 'cat' from English to Spanish."

"Who is on the Spanish leaderboard today?"

Troubleshooting Duolingo MCP Server with CrewAI

Common issues when connecting Duolingo to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Duolingo + CrewAI FAQ

Common questions about integrating Duolingo MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.