How to Use the BLS Public Data API MCP in Pydantic AI

Q: How do I initialize the BLS Public Data API toolset in Pydantic AI?

Run pip install "pydantic-ai-slimmcp" to get the required dependencies. Then, instantiate your connection using MCPToolset with your Vinkius URL and pass it to your Agent.

Bring strict, runtime-validated BLS labor statistics to your Pydantic AI agents via this dedicated MCP Server.

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Connect BLS Public Data API MCP to Pydantic AI

Create your Vinkius account to connect BLS Public Data API 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 Public Data API with Pydantic AI

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Type-Safe Economic Data Fetching

The `get_bls_timeseries_data` tool retrieves raw labor statistics and feeds them directly into your type-safe agent pipelines. Pydantic AI validates the structure of this incoming data at runtime against strict schemas. If the federal API returns an unexpected payload structure, your agent fails loudly immediately. This prevents silent data corruption from creeping into your downstream financial models and reports.

Checking Server Availability Safely

The `check_api_status` tool lets your agent confirm the BLS system is online before attempting complex data extractions. The response is parsed and validated, ensuring your agent knows the exact state of the remote service. Because Pydantic AI enforces strict validation, your agent won't get confused by malformed HTML error pages if the government server is experiencing an outage. It either gets a clean status or a clear validation error.

Building a Reliable MCP Server Integration

This MCP Server integrates with your agent using the unified `MCPToolset` class. This approach replaces deprecated HTTP classes and provides a clean, future-proof connection to your managed Vinkius endpoint. You can run your agent with any supported LLM while maintaining absolute confidence in your data parsing. The model-agnostic validation layer ensures that whether you use open-source or proprietary models, the data remains clean.

Setup guide

Set up BLS Public Data API 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-public-data-api-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent BLS Public Data API transactions")
print(result.output)

Get your connection token →

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Common questions about BLS Public Data API MCP in Pydantic AI

Run `pip install "pydantic-ai-slim[mcp]"` to get the required dependencies. Then, instantiate your connection using `MCPToolset` with your Vinkius URL and pass it to your Agent.

The framework will raise a validation error instantly. This guarantees your agent never processes corrupt economic series data or hallucinates missing values.

Yes, Pydantic AI is completely model-agnostic. You can run local models or proprietary APIs, and they will all interact with the tools through the same validated interface.

Yes, the toolset supports both Streamable HTTP and SSE transports. You just need to point your `MCPToolset` configuration to the correct Vinkius endpoint.

All communication goes through a secure, ephemeral V8 sandbox managed by Vinkius. Your requested series IDs and the returned labor statistics are never written to disk or used for training.

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