How to Use the BLS Jobs — Nonfarm Payrolls & Wages MCP in LlamaIndex

Q: How do I index data from BLS Jobs — Nonfarm Payrolls & Wages into LlamaIndex?

You run the getnonfarmpayrolls tool via McpToolSpec and pass the raw JSON output to your LlamaIndex document indexer. This converts the employment data into vector nodes for semantic search.

Q: Can LlamaIndex query historical series from BLS Jobs — Nonfarm Payrolls & Wages?

Yes, using the querybls tool, your LlamaIndex agent can retrieve up to 50 series IDs at once. The retrieved timeseries data is vectorized and stored, allowing you to run RAG queries over historical payroll trends.

Q: Does the tool support filtering specific payroll series?

Yes, you can pass specific BLS Series IDs to the querybls tool. Your LlamaIndex agent dynamically determines which IDs to query based on the user's natural language question.

Index live BLS payroll data directly into your LlamaIndex vector stores for ground-truth financial RAG.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect BLS Jobs — Nonfarm Payrolls & Wages MCP to LlamaIndex

Create your Vinkius account to connect BLS Jobs — Nonfarm Payrolls & Wages 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 Jobs — Nonfarm Payrolls & Wages with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Index US payroll metrics into LlamaIndex vector stores

The `get_nonfarm_payrolls` tool fetches the latest CES0000000001 employment numbers and writes them directly into your index. By using this MCP Server, LlamaIndex vectorizes these raw metrics, allowing your RAG pipeline to retrieve actual government data instead of relying on outdated model weights. This setup prevents hallucinated employment numbers in your market reports. Your agent pulls the exact payroll figures, indexes them, and references them during semantic search queries.

Query historical BLS timeseries with LlamaIndex RAG

The `query_bls` tool lets your agent pull up to 50 concurrent economic series directly into your document store. LlamaIndex structures these timeseries outputs as document nodes, making them instantly searchable alongside your PDF research papers. Your agent queries this hybrid index to find correlations between wages and employment growth. You avoid manual data cleaning because the MCP Server outputs clean, structured JSON ready for indexing.

Ground financial analysis in real-time employment data

The `get_nonfarm_payrolls` tool provides the ground-truth economic metrics your agent needs to answer complex macro questions. By wrapping this tool in a LlamaIndex agent, you ensure that every synthesized answer is backed by verified BLS data. The agent checks the latest payroll figures before writing its analysis. It maps the raw series values directly to your financial templates, ensuring your reports are consistently accurate.

Setup guide

Set up BLS Jobs — Nonfarm Payrolls & Wages 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 Jobs — Nonfarm Payrolls & Wages 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 Jobs — Nonfarm Payrolls & Wages tools.",
)
response = await agent.run("List recent BLS Jobs — Nonfarm Payrolls & Wages 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 Jobs — Nonfarm Payrolls & Wages 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 Jobs — Nonfarm Payrolls & Wages MCP in LlamaIndex

You run the `get_nonfarm_payrolls` tool via McpToolSpec and pass the raw JSON output to your LlamaIndex document indexer. This converts the employment data into vector nodes for semantic search.

Yes, using the `query_bls` tool, your LlamaIndex agent can retrieve up to 50 series IDs at once. The retrieved timeseries data is vectorized and stored, allowing you to run RAG queries over historical payroll trends.

Install llama-index-tools-mcp, initialize the client with your Vinkius URL, and convert it using McpToolSpec. You then pass the tools to your FunctionAgent for execution.

Yes, you can pass specific BLS Series IDs to the `query_bls` tool. Your LlamaIndex agent dynamically determines which IDs to query based on the user's natural language question.

Vinkius processes all requests in an ephemeral sandbox. Your custom BLS series queries and payroll outputs are processed in memory and never stored, keeping your proprietary market indicators private.

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