How to Use the Didacte MCP in CrewAI

Q: How do I connect the Didacte MCP Server to my CrewAI agents?

Pass your Vinkius MCP server URL directly into the mcps list when defining your CrewAI Agent. For advanced setups, use MCPServerHTTP from crewai.mcp to filter specific tools for each agent.

Q: Can a CrewAI agent search Didacte courses by keyword?

Yes. The searchcoursesbytitle tool allows specialized agents to find courses using title keywords, making it easy to automate content discovery within your multi-agent workflows.

Deploy a crew of specialized AI agents to manage Didacte courses and track student performance autonomously with CrewAI.

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Connect Didacte MCP to CrewAI

Create your Vinkius account to connect Didacte 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 Didacte with CrewAI

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Coordinate multi-agent course audits

The `list_lms_courses` tool allows a researcher agent in your CrewAI team to scan your entire Didacte catalog. The agent compiles the course list and hands it off to an analyst agent to identify outdated content. The analyst then calls `list_course_curriculum` to inspect the lessons and modules of specific Didacte courses within the CrewAI pipeline. This collaborative workflow runs entirely in the background, generating reports without human intervention.

Automate student performance monitoring

The `get_user_learning_profile` tool gives your CrewAI monitoring agent immediate access to individual student summaries. The agent analyzes these Didacte profiles to track completion rates across your organization. Together, these CrewAI agents manage student engagement and flag learners who need extra assistance in Didacte. A separate moderator agent can use `list_active_learning_progress` to see who has worked on lessons recently.

Manage course rosters with this MCP Server

The `list_course_enrollments` tool provides your CrewAI agents with a complete list of users in any class. An administrative agent can compare this Didacte list against your corporate directory to find missing enrollments. If gaps are found, the CrewAI agent uses `search_courses_by_title` to locate the correct Didacte training modules and queue up enrollment tasks. This keeps your team's training records synchronized across platforms.

Setup guide

Set up Didacte 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 Didacte tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent Didacte 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 →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Didacte. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Didacte MCP in CrewAI

Pass your Vinkius MCP server URL directly into the `mcps` list when defining your CrewAI Agent. For advanced setups, use `MCPServerHTTP` from `crewai.mcp` to filter specific tools for each agent.

Yes. The `search_courses_by_title` tool allows specialized agents to find courses using title keywords, making it easy to automate content discovery within your multi-agent workflows.

Agents share data via CrewAI's built-in memory system. When one agent runs `list_user_enrollments`, the resulting course list is stored and made accessible to other agents in the crew.

Yes. You can use CrewAI's tool filter with our managed MCP server to limit an agent's access. For example, you can allow a reporting agent to use only `get_account_metadata` while blocking admin tools.

All student enrollment and course progress data is processed within ephemeral Vinkius V8 Isolate sandboxes running your MCP server. Your Workleap API tokens are stored securely and never exposed to the LLM or external networks.

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