How to Use the Degreed MCP in Pydantic AI

Q: Can I use Pydantic AI to validate Degreed learning pathways?

Yes. When your agent calls listlearningpathways, Pydantic AI validates the pathway objectives and durations against your typed schemas, ensuring your pipeline gets structured data.

Run type-safe Degreed workflows with Pydantic AI and catch API mismatches instantly.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Degreed MCP to Pydantic AI

Create your Vinkius account to connect Degreed to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Degreed with Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Type-safe skill tracking with Pydantic AI

Avoid silent failures when tracking employee skills. Pydantic AI validates every payload returned by `get_user_profile` and `list_defined_skills` against strict Python schemas. If the API returns a malformed skill rating, the framework raises a validation error immediately. This strict validation ensures that your agent only processes clean data when mapping organizational talent. You can confidently run automated talent reviews knowing that bad API payloads will never corrupt your internal databases.

Schema validation for Degreed MCP Server catalogs

Searching large learning catalogs can lead to unpredictable outputs. This MCP Server integration forces all results from `search_learning_catalog` and `list_learning_content` to conform to your defined Pydantic models. Your agent can extract course URLs, titles, and durations using `get_content_details` without risking hallucinated fields. If a course is missing a mandatory metadata field, Pydantic AI catches it at the boundary before your application code runs.

Reliable completion audits with Pydantic AI

Auditing corporate compliance requires absolute precision. The framework validates the completion history returned by `list_user_completions` and `list_active_learners` to ensure every record contains valid timestamps and skill points. When planning future training via `list_learning_plans` using the MCP, the agent checks deadlines against strict datetime models. This prevents your agent from scheduling courses on invalid dates or assigning plans to non-existent user IDs.

Setup guide

Set up Degreed MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "degreed-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Degreed tools.",
)

result = await agent.run("List recent Degreed transactions")
print(result.output)

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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Degreed now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Degreed MCP in Pydantic AI

Use the MCPToolset class pointing to your Vinkius HTTP endpoint. Pass this toolset directly to your Agent constructor to give your model access to all ten Degreed tools.

The framework will immediately raise a validation error at runtime. This prevents your agent from acting on corrupt user profiles or malformed completion lists returned by list_user_completions.

Yes. When your agent calls list_learning_pathways, Pydantic AI validates the pathway objectives and durations against your typed schemas, ensuring your pipeline gets structured data.

Yes, the toolset class supports both Streamable HTTP and SSE transports. You can connect your externally running Vinkius server using either protocol depending on your network setup.

All user system IDs, professional titles, and skill ratings are retrieved through secure V8 isolates. Vinkius handles the credentials on its zero-trust platform, so your Pydantic AI code only receives validated payloads without storing raw API tokens.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript