How to Use the Workday MCP in Pydantic AI

Q: How can I find pending time off requests in Workday using Pydantic AI?

You call gettimeoffrequests. The agent gets the raw data from the MCP Server, but before your Python code sees it, Pydantic validates that every field—like request dates or status codes—is correct and typed properly.

Q: Does this Workday server handle listing open job requisitions with Pydantic AI?

Yes, the listjobrequisitions tool is available. The returned list of jobs will be validated against your model, ensuring you can reliably process things like requisition IDs and status dates.

Q: If I search for workers in Workday using Pydantic AI, what data type am I getting?

You use listworkers. The response is validated against your defined worker profile model. This guarantees you get specific fields like names and IDs that your agent expects, every single time.

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Connect Workday MCP to Pydantic AI

Create your Vinkius account to connect Workday 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.

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Setup Workday with Pydantic AI

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Get Payroll and Time Off Balances from the MCP Server

The `get_payroll_results` tool pulls wage data for a specific worker, but because you use Pydantic AI, the output is validated. If Workday sends back weird data, your agent fails loudly—no silent corruption. Similarly, calling `get_time_off_balances` guarantees that the resulting PTO totals match the expected Python data model, making your code reliable.

Manage Worker and Organizational Data Safely

Need to know an employee's profile? Use `get_worker`. The response is type-checked instantly against a Pydantic schema. You don't have to worry about receiving unexpected fields or data types. For organizational context, you can list supervisors using `list_supervisory_orgs`, and the returned structure will always match your defined model.

Query Specific Workday Resources with Confidence

The `query_resource` tool handles general API requests—locations, cost centers, business units. With Pydantic AI, you get confidence that whatever data type is returned (whether it's a list or a single record), it conforms to your schema. This makes building production agents much easier because the data structure is guaranteed.

Setup guide

Set up Workday 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": {
        "workday-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Workday MCP in Pydantic AI

You call `get_time_off_requests`. The agent gets the raw data from the MCP Server, but before your Python code sees it, Pydantic validates that every field—like request dates or status codes—is correct and typed properly.

The main benefit is correctness. If Workday changes its API response format in a way that breaks your existing code, Pydantic fails immediately and loudly. You won't get silent data corruption.

Yes, the `list_job_requisitions` tool is available. The returned list of jobs will be validated against your model, ensuring you can reliably process things like requisition IDs and status dates.

You use `list_workers`. The response is validated against your defined worker profile model. This guarantees you get specific fields like names and IDs that your agent expects, every single time.

The server handles payroll, time-off balances, worker profiles, job requisition lists, and organizational hierarchy. All these results are passed through a strict type-checking layer.

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