How to Use the BLS Wages — OEWS Occupational Employment MCP in Pydantic AI

Ensure type-safe wage lookups by connecting Pydantic AI to the BLS Wages — OEWS Occupational Employment server.

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Connect BLS Wages — OEWS Occupational Employment MCP to Pydantic AI

Create your Vinkius account to connect BLS Wages — OEWS Occupational Employment 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 BLS Wages — OEWS Occupational Employment with Pydantic AI

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Claude

Perplexity

Type-safe `query_bls` for Pydantic AI

You define a Pydantic model for your wage data, and this server ensures the API response matches your schema perfectly. No more guessing what the data looks like. If the BLS server returns a field you didn't expect, your agent fails fast. This keeps your production systems clean and prevents silent data corruption.

Strict validation for wage statistics

Every response from `query_bls` is validated against your Pydantic models at runtime. Your agent only acts on data that meets your strict requirements. This is essential for building robust hiring pipelines. You get a guarantee that the median earnings you pull are exactly the type and format your logic expects.

Flexible transport for Pydantic AI

You can run your server over Streamable HTTP or SSE. Pydantic AI handles the connection, allowing your agent to stay model-agnostic. Whether you use local models or external APIs, your agent interacts with the wage data in the same way. The consistency keeps your codebase manageable.

Setup guide

Set up BLS Wages — OEWS Occupational Employment 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": {
        "bls-wages-oews-occupational-employment-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to BLS Wages — OEWS Occupational Employment tools.",
)

result = await agent.run("List recent BLS Wages — OEWS Occupational Employment transactions")
print(result.output)

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Common questions about BLS Wages — OEWS Occupational Employment MCP in Pydantic AI

Pydantic AI checks every tool response against your defined models. If the data doesn't match your schema, the agent throws a validation error immediately.

Yes. Because the MCP toolset is model-agnostic, you can swap between OpenAI, Anthropic, or local models without changing how you query the wage data.

The server runs externally, and your agent connects via the toolset. You define the transport, and the agent manages the lifecycle of each request.

Your agent waits for the response within the constraints you set. You can implement timeouts in your Pydantic AI agent logic to handle any latency.

This server handles the endpoint authentication for you. It only touches public wage statistics, ensuring no private financial data is ever at risk.

Use it with your favorite AI tools

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