How to Use the BLS JOLTS — Job Openings, Quits & Turnover MCP in LlamaIndex

Q: Can I index historical data from the BLS JOLTS — Job Openings, Quits & Turnover tools?

Yes. You can run querybls for historical series, capture the timeseries outputs, and load them into a vector index for semantic search.

Q: Is there a limit on how many series IDs I can query at once?

The querybls tool allows up to 50 concurrent lookbacks in a single call. This lets you pull massive timeseries datasets without hitting rate limits.

Index live BLS JOLTS labor data directly into your LlamaIndex vector stores for ground-truth synthesis.

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Connect BLS JOLTS — Job Openings, Quits & Turnover MCP to LlamaIndex

Create your Vinkius account to connect BLS JOLTS — Job Openings, Quits & Turnover 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 BLS JOLTS — Job Openings, Quits & Turnover with LlamaIndex

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Index JOLTS metrics into your knowledge base

The `get_jolts_data` tool fetches national-level metrics on quits and hires to ground your agent's answers in actual government data. LlamaIndex takes this raw output and converts it into document nodes for immediate vector search. This process prevents your agent from hallucinating labor statistics. Instead of guessing, the engine queries the indexed JOLTS data to answer specific questions about the Great Resignation.

Query raw BLS series codes via this MCP Server

The `query_bls` tool handles custom, low-level queries using explicit series IDs. If your application needs historical trends on specific industries, your agent uses this tool to pull up to 50 concurrent lookbacks. LlamaIndex indexes these timeseries results immediately. Your application can then compare historical labor patterns against current market documents in a unified query pipeline.

Combine live labor data with static documents

Your agent uses `get_jolts_data` to fetch current hire and fire rates during a live query session. The framework merges this live feed with your existing local PDFs or database records. This hybrid approach ensures your financial models reflect the latest official government numbers. You get real-time context without manually updating your vector store files.

Setup guide

Set up BLS JOLTS — Job Openings, Quits & Turnover 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 JOLTS — Job Openings, Quits & Turnover 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 JOLTS — Job Openings, Quits & Turnover tools.",
)
response = await agent.run("List recent BLS JOLTS — Job Openings, Quits & Turnover data")

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Common questions about BLS JOLTS — Job Openings, Quits & Turnover MCP in LlamaIndex

Use the llama-index-tools-mcp package to initialize the basic client with your Vinkius URL. Then, convert the tools to a list and pass them to your FunctionAgent.

Yes. You can run `query_bls` for historical series, capture the timeseries outputs, and load them into a vector index for semantic search.

By providing direct access to official government metrics. Your agent queries the live API instead of relying on its pre-trained weights for labor statistics.

The `query_bls` tool allows up to 50 concurrent lookbacks in a single call. This lets you pull massive timeseries datasets without hitting rate limits.

Yes. This integration only transmits numerical series IDs and public labor metrics. Vinkius runs the MCP Server in an isolated sandbox, keeping your proprietary search parameters secure.

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