How to Use the Degreed MCP in CrewAI

Q: Can I build a CrewAI team to find new Degreed content and analyze it?

Yes, that's a perfect use case. One agent can use searchlearningcatalog to find content, and another can use getcontentdetails to analyze its metadata. The agents collaborate to filter and rank the results.

Q: How would a CrewAI agent get a user's skill profile from Degreed?

The agent would be given access to the getuserprofile tool. When its task requires that data, it can call the tool with a user's ID to get their assigned skills and progress.

Deploy a crew of autonomous agents to manage your Degreed learning ecosystem with CrewAI.

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Connect Degreed MCP to CrewAI

Create your Vinkius account to connect Degreed 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 Degreed with CrewAI

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Deploy a Content Curation Crew

Assemble a CrewAI team to find and vet new training content. For example, a 'Researcher' agent can run `search_learning_catalog` every day for new articles on a specific topic. It passes any findings to a 'Reviewer' agent. The 'Reviewer' then uses `get_content_details` to check the content's length, format, and skill tags. If it meets your team's criteria, it can pass the item to a 'Publisher' agent that adds it to a shared list for your human team to review. The agents handle the busywork.

A Team for Proactive Skill Audits

You can build a crew that monitors your team's skill development without any manual oversight. A 'Monitor' agent runs `list_active_learners` once a week. If it finds someone who hasn't completed anything recently, it passes that user's ID to an 'Analyst' agent. The 'Analyst' agent's job is to investigate. It uses `get_user_profile` and `list_learning_plans` to see what that person is supposed to be working on. It can then pass a summary to a 'Notifier' agent that alerts a manager. This entire MCP workflow runs autonomously.

Onboarding and Compliance Crew

Split up the work of managing new hire training. An 'Onboarding' agent can be responsible for finding the right `list_learning_pathways` for new employees and tracking their general progress with `list_user_completions`. At the same time, a specialized 'Compliance' agent can run in parallel. Its only job is to watch for the completion of mandatory security or HR training. If a deadline is near, it takes priority and sends escalating reminders. CrewAI lets you assign these distinct roles, and this MCP server gives them the data to act.

Setup guide

Set up Degreed 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 Degreed tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent Degreed 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 Degreed. 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 Degreed MCP in CrewAI

Yes, that's a perfect use case. One agent can use `search_learning_catalog` to find content, and another can use `get_content_details` to analyze its metadata. The agents collaborate to filter and rank the results.

The agent would be given access to the `get_user_profile` tool. When its task requires that data, it can call the tool with a user's ID to get their assigned skills and progress.

Yes, that's how CrewAI is designed to work. For example, a 'Monitor' agent can use `list_active_learners` and pass user IDs to a 'Notifier' agent if certain conditions are met. The agents share context to complete the multi-step task.

When you configure the MCP server in CrewAI, you can use the `tool_filter` argument. This lets you define exactly which tools from the Degreed server each specific agent in your crew is allowed to use.

Your crew only interacts with Degreed user profiles and learning activity data. The Vinkius MCP Server connection is stateless; each session is isolated and runs in a sandboxed environment, ensuring data isn't persisted after your crew completes its task.

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