How to Use the BLS Labor Force — National Unemployment & CPS MCP in LlamaIndex

Q: How do I index BLS Labor Force — National Unemployment & CPS data into LlamaIndex using MCP?

You use the McpToolSpec to fetch data via querybls, then load the tool's raw text response into your document parser. From there, it is vectorized and stored like any other node.

Q: Can LlamaIndex query engines automatically call the BLS Labor Force — National Unemployment & CPS tools?

Yes. When wrapped in McpToolSpec, tools like getunemploymentrate are exposed to LlamaIndex's FunctionAgent, which calls them based on user queries.

Q: Does the BLS Labor Force — National Unemployment & CPS server support LlamaIndex's asynchronous tool calling?

Yes. You can call totoollistasync on the tool spec, allowing your application to fetch economic series data without blocking other query operations.

Q: Can I filter which BLS tools are available to my LlamaIndex agent?

Yes, you can use the allowedtools filter during setup to restrict your agent to only the getunemploymentrate tool if you do not want it executing custom series queries.

Index real-time US labor market data directly into your LlamaIndex knowledge base with this MCP Server.

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Connect BLS Labor Force — National Unemployment & CPS MCP to LlamaIndex

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Setup BLS Labor Force — National Unemployment & CPS with LlamaIndex

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Ground LlamaIndex RAG in live CPS data

The `get_unemployment_rate` tool retrieves the exact national unemployment rate (LNS14000000) from the CPS database. Your LlamaIndex agent can pull this live data on demand, ensuring your semantic searches return answers based on current government figures instead of stale vector embeddings. The tool output becomes a queryable node in your index. This means your LLM can cross-reference live labor force participation rates with your internal documents, giving you a unified view of how macroeconomic trends affect your business.

Index 50 BLS timeseries concurrently with LlamaIndex

The `query_bls` tool enables concurrent lookups of up to 50 timeseries using explicit BLS Series IDs. LlamaIndex takes this raw numerical data, converts it into structured documents, and indexes it for semantic search, allowing your user to ask natural language questions about complex labor trends. Since the MCP integration handles schema conversion, the timeseries data fits neatly into your existing index structure. You don't have to write custom parsers for the BLS v2 API payload; the tool output is ready for retrieval out of the box.

Connect this MCP Server to LlamaIndex query engines

This MCP Server provides the `get_unemployment_rate` tool to keep your vector index updated with real-time macroeconomic statistics. Instead of relying on static vector stores, your LlamaIndex agent can decide to run a live lookup when a user asks about the current job market. The integration supports the McpToolSpec wrapper, making these tools look like native LlamaIndex tools. Your query engine can transition from searching local PDFs to querying live government databases without breaking the execution flow.

Setup guide

Set up BLS Labor Force — National Unemployment & CPS 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 Labor Force — National Unemployment & CPS 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 Labor Force — National Unemployment & CPS tools.",
)
response = await agent.run("List recent BLS Labor Force — National Unemployment & CPS data")

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Common questions about BLS Labor Force — National Unemployment & CPS MCP in LlamaIndex

You use the McpToolSpec to fetch data via `query_bls`, then load the tool's raw text response into your document parser. From there, it is vectorized and stored like any other node.

Yes. When wrapped in McpToolSpec, tools like `get_unemployment_rate` are exposed to LlamaIndex's FunctionAgent, which calls them based on user queries.

Yes. You can call `to_tool_list_async` on the tool spec, allowing your application to fetch economic series data without blocking other query operations.

Yes, you can use the allowed_tools filter during setup to restrict your agent to only the `get_unemployment_rate` tool if you do not want it executing custom series queries.

All communications with the server go through Vinkius's zero-trust gateway. Your BLS Series IDs and query parameters are encrypted in transit and processed in ephemeral, isolated runtimes that delete all session data immediately after execution.

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