How to Use the BLS Local — LAUS State & County Unemployment MCP in Pydantic AI

Query verified county unemployment data with absolute type safety using Pydantic AI and MCP.

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Connect BLS Local — LAUS State & County Unemployment MCP to Pydantic AI

Create your Vinkius account to connect BLS Local — LAUS State & County Unemployment 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 Local — LAUS State & County Unemployment with Pydantic AI

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Type-safe labor data queries in Pydantic AI

The `query_bls` tool delivers structured timeseries data from the LAUS program directly into your validated agent models. This MCP Server ensures every state, county, and metro unemployment metric is parsed against your strict schemas, preventing malformed API responses from corrupting your database. If the government endpoint changes its response format, your agent fails immediately and loudly. This strict runtime validation keeps dirty data from silently polluting your analytical pipelines.

Strict validation for 50 concurrent series

The `query_bls` tool handles complex series lookups without sacrificing type safety. When you call the tool with multiple series IDs, this MCP Server ensures every single data point matches your defined Pydantic models before returning the final payload. You can confidently run the maximum 50 concurrent lookbacks. The framework validates the entire batch in parallel, giving you clean, typed data arrays ready for immediate math operations.

Model-agnostic economic analytics

The `query_bls` tool works across any LLM provider you choose to run with your agent. Whether you use local models or commercial APIs, the underlying data schema remains identical and fully validated. This decoupling lets you swap models without rewriting your data fetching logic. The tool handles the raw HTTP connection to the BLS database, while your agent focuses entirely on analyzing the clean results.

Setup guide

Set up BLS Local — LAUS State & County Unemployment 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-local-laus-state-county-unemployment-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to BLS Local — LAUS State & County Unemployment tools.",
)

result = await agent.run("List recent BLS Local — LAUS State & County Unemployment transactions")
print(result.output)

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Common questions about BLS Local — LAUS State & County Unemployment MCP in Pydantic AI

Use the unified toolset class to register the server's HTTP endpoint. Pass this toolset directly to your agent constructor to make the query tools available.

The server returns structured JSON, which the framework then validates against your runtime schemas. This ensures your code only processes correctly formatted unemployment rates.

This server supports both streamable HTTP and SSE transports. You can connect your production agents to our managed endpoints using the protocol that fits your hosting setup.

The server manages connection pools, but you should handle rate-limit exceptions in your agent logic. The tool returns standard HTTP status codes when limits are hit.

No, all schema validation happens locally within your runtime environment. The raw timeseries data and requested series IDs are never exposed to external validation APIs or logged by Vinkius.

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