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

Q: How do I index data from BLS Wages — OEWS Occupational Employment into my LlamaIndex vector store?

You use the querybls tool to retrieve raw timeseries data, convert the JSON payload into LlamaIndex Document nodes, and then ingest them into your vector database. This keeps your local salary index updated with verified federal figures.

Index live BLS occupational wage data directly into your LlamaIndex vector stores for hallucination-free HR analysis.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect BLS Wages — OEWS Occupational Employment MCP to LlamaIndex

Create your Vinkius account to connect BLS Wages — OEWS Occupational Employment 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.

GDPR Free for Subscribers

Setup BLS Wages — OEWS Occupational Employment with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Index federal wage timeseries into LlamaIndex

The `query_bls` tool pulls precise median earnings data directly into your LlamaIndex document pipeline. Instead of relying on static PDFs or outdated training data, your agent queries the live BLS timeseries and stores the raw numbers as searchable nodes. This prevents your HR agents from hallucinating salary ranges during candidate evaluations. You can query your index for specific regional wage benchmarks and receive answers grounded in actual, live government data.

Build RAG applications with this MCP Server

Integrating this MCP Server into your LlamaIndex setup lets you combine static internal documents with live market rates. Your agent can pull internal salary bands from a vector store, then call `query_bls` to fetch the corresponding federal market median. The tool handles up to 50 concurrent lookbacks, allowing your RAG pipeline to assemble complex market-comparison reports in a single query loop. Your final output stays accurate because it is built on verified, structured data.

Retrieve structured salary tables for semantic search

LlamaIndex developers can use the `query_bls` output to build structured tables within their index. This structured format makes it easy to perform semantic searches over historical wage trends across different states. Your retrieval pipeline can fetch these wage nodes based on user queries like finding the median salary for software developers in Texas. The agent gets the exact numbers it needs without parsing unstructured text.

Setup guide

Set up BLS Wages — OEWS Occupational Employment 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 BLS Wages — OEWS Occupational Employment 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 BLS Wages — OEWS Occupational Employment tools.",
)
response = await agent.run("List recent BLS Wages — OEWS Occupational Employment data")

Get your connection token →

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

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect BLS Wages — OEWS Occupational Employment now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about BLS Wages — OEWS Occupational Employment MCP in LlamaIndex

You use the `query_bls` tool to retrieve raw timeseries data, convert the JSON payload into LlamaIndex Document nodes, and then ingest them into your vector database. This keeps your local salary index updated with verified federal figures.

Your LlamaIndex agent must first locate the correct numerical series ID, which it then passes to the `query_bls` tool. This ensures the agent retrieves the exact occupational wage data you need without guessing.

It replaces static, outdated salary documents with live timeseries data fetched directly via `query_bls`. LlamaIndex uses these fresh metrics to ground its answers, eliminating outdated wage estimates.

Yes, the tool supports up to 50 concurrent lookbacks. Your LlamaIndex agent can fetch data for dozens of occupations simultaneously, preventing bottlenecks in your data ingestion pipeline.

This MCP Server only processes public BLS series IDs to fetch federal wage statistics. Your proprietary company salary data stays entirely within your private LlamaIndex vector store and is never sent to the public API.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript