How to Use the DOL (Department of Labor) MCP in LlamaIndex

Q: How do I index DOL (Department of Labor) records into LlamaIndex?

Use McpToolSpec to load tools like getoshainspections into your framework. The outputs are returned as documents, which you can index directly into your vector store for semantic retrieval.

Q: Can I use LlamaIndex to query archived DOL (Department of Labor) data?

Yes. By indexing the outputs of getwhdcompliance over time, LlamaIndex lets you run semantic search queries across your local archive of wage and hour investigations.

Q: Does LlamaIndex support async tools for DOL (Department of Labor) lookups?

Yes. You can call totoollistasync() on your tool spec to fetch data from getmshainspections asynchronously, preventing bottlenecks during large-scale data ingestion.

Q: How do I filter which tools the agent can access?

You can restrict agent access by using the allowedtools filter when initializing your tool spec. This ensures the agent only calls the specific federal endpoints your workflow requires.

Index live DOL (Department of Labor) safety and wage data directly into LlamaIndex vector stores for semantic RAG queries.

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Connect DOL (Department of Labor) MCP to LlamaIndex

Create your Vinkius account to connect DOL (Department of Labor) to LlamaIndex 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 DOL (Department of Labor) with LlamaIndex

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Build a LlamaIndex RAG index from live DOL MCP Server data

Stop relying on outdated static documents for regulatory research. Your LlamaIndex agent calls `get_whd_compliance` to pull active wage investigations, then writes those records straight into your vector index to ground future answers. This turns raw federal API responses into a searchable knowledge base. When you query your index about regional compliance trends, the system retrieves actual, live data points instead of hallucinating historical facts.

Semantic search on mine violations

Use LlamaIndex to query complex safety records retrieved via `get_msha_violations`. The framework indexes the raw violation descriptions alongside inspection histories pulled from `get_msha_inspections`. You can then run semantic queries over these indexed safety records. Instead of hunting through database tables, you ask your agent which mines show patterns of negligence and get answers backed by indexed source records.

Grounded workplace safety analysis

Ensure your safety reports are grounded in actual federal enforcement data. The agent invokes `get_osha_inspections` to pull active workplace hazard records, indexing them directly into your local document store. This prevents the agent from making up safety codes or citation histories. Every answer generated by LlamaIndex references the exact inspection numbers and citation dates retrieved from the federal server.

Setup guide

Set up DOL (Department of Labor) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all DOL (Department of Labor) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to DOL (Department of Labor) tools.",
)
response = await agent.run("List recent DOL (Department of Labor) data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by DOL (Department of Labor). 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.

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Common questions about DOL (Department of Labor) MCP in LlamaIndex

Use `McpToolSpec` to load tools like `get_osha_inspections` into your framework. The outputs are returned as documents, which you can index directly into your vector store for semantic retrieval.

Yes. By indexing the outputs of `get_whd_compliance` over time, LlamaIndex lets you run semantic search queries across your local archive of wage and hour investigations.

Yes. You can call `to_tool_list_async()` on your tool spec to fetch data from `get_msha_inspections` asynchronously, preventing bottlenecks during large-scale data ingestion.

You can restrict agent access by using the `allowed_tools` filter when initializing your tool spec. This ensures the agent only calls the specific federal endpoints your workflow requires.

The safety inspection data and wage logs retrieved from the federal endpoints are processed in ephemeral, single-use V8 environments. No record of your specific compliance queries is stored on the host servers.

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