BLS Local — LAUS MCP. Analyze unemployment rates by county, state, and MSA.
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The BLS Local — LAUS State & County Unemployment MCP Server lets you query granular unemployment data across any US State, County, or Metropolitan Statistical Area.
Instead of looking at national averages, you get hyper-localized labor market insights from the Bureau of Labor Statistics (BLS). Use this to compare unemployment rates between specific counties like Miami-Dade and Cook, or analyze trends across different MSAs.
It’s designed for deep, regional economic analysis.
What your AI agents can do
Query bls
Queries the generic BLS v2 API to pull time-series labor statistics using explicit BLS Series IDs.
The query_bls tool fetches historical unemployment data points for specific BLS Series IDs across defined geographic areas.
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BLS Local — LAUS MCP Server: 1 Tool for Regional Statistics
Use the `query_bls` tool to pull structured, historical labor market statistics for specific regions and time periods.
019d755fquery bls
Queries the generic BLS v2 API to pull time-series labor statistics using explicit BLS Series IDs.
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What you can do with this MCP connector
The BLS Local — LAUS State & County Unemployment MCP Server lets your AI client pull granular unemployment data from the Bureau of Labor Statistics (BLS) for any US State, County, or Metropolitan Statistical Area. Instead of looking at national averages, you get hyper-localized labor market insights. The query_bls tool pulls time-series labor statistics using explicit BLS Series IDs, letting you compare unemployment rates across specific counties like Miami-Dade and Cook, or analyze trends across different MSAs.
You can use it to compare labor trends between states like California and Texas. You can also get detailed comparisons between counties, such as Miami-Dade and Cook County. The server exposes structured access to the raw time-series data needed for deep, regional economic analysis.
How BLS Local — LAUS MCP Works
- 1 Your AI client calls
query_blsand specifies the required BLS Series IDs, geographic codes, and date ranges. - 2 The server executes the query against the BLS API, pulling the raw time-series data points.
- 3 You receive the structured data output, which your agent can then use to analyze and compare regional trends.
The bottom line is you can pull specific, structured labor statistics for any US region, letting your agent work with the raw numbers.
Who Is BLS Local — LAUS MCP For?
Economic modelers, data analysts, and market researchers need this. If your job involves comparing labor market performance between specific counties or states, this server is essential. It cuts through the national noise and gives you the granular data required to build accurate regional economic reports.
Uses the tool to compare unemployment rates between specific MSAs or countires to pinpoint localized economic stress points for investment reports.
Checks historical unemployment data across different counties to forecast labor needs and guide infrastructure development in growing metro areas.
Retrieves time-series employment data for multiple states to model regional economic risk exposure for a portfolio of investments.
What Changes When You Connect
- See how specific counties compare. Instead of a national average, you pull granular data comparing Miami-Dade vs Cook County, allowing for focused regional analysis.
- Track state-level changes over time. You can compare California's labor trends against Texas, pulling direct, comparable data points for deep market reports.
- Isolate Metropolitan Statistical Areas (MSAs). The server lets you focus on specific metro regions, like analyzing the labor shifts in the Chicago MSA without the noise from surrounding areas.
- Get structured, time-series data. The
query_blstool gives you raw API data, not just summarized text. You're working with numbers, which is what you need for modeling. - Handle complex comparisons. You can run queries that track multiple variables (e.g., unemployment rate AND labor force participation) across different regions simultaneously.
- Build data-backed narratives. Because the data is sourced from the BLS, your reports carry the weight of official government statistics.
Real-World Use Cases
Modeling Interstate Economic Divergence
A financial risk manager needs to compare labor market health between Florida and Georgia. They prompt their agent: 'What was the unemployment trend in both FL and GA over the last 5 years?' The agent uses query_bls to pull the time-series data for both states, allowing the model to spot divergence points that signal differing economic risks.
Targeting Growth in Specific Metro Areas
An urban planner wants to know which MSAs are recovering fastest post-pandemic. They ask the agent to 'Identify MSAs with unemployment rates below 3.5% over the last 12 months.' The agent calls query_bls to scan multiple metro areas, quickly identifying high-growth, low-unemployment zones for new development plans.
Analyzing County-Specific Labor Shifts
A consulting firm needs to audit labor stability in a specific region. They instruct their agent to 'Compare the historical unemployment rates of Miami-Dade County versus a nearby county like Broward.' The agent runs two focused query_bls calls, providing the necessary data to pinpoint localized economic weaknesses.
Evaluating Sectoral Performance Post-Crisis
A researcher wants to know how the tech sector's impact differed between New York and Massachusetts. They ask the agent to pull the time-series data for key employment categories in both states. The resulting query_bls data lets the researcher model which state recovered faster based on specific job growth metrics.
The Tradeoffs
Assuming national data is enough
Asking the agent, 'What's the unemployment rate in the Southeast?' The agent pulls a single national average, which masks critical variations between cities like Jacksonville and Savannah.
→
Instead, prompt: 'Pull the LAUS data for the MSAs in the Southeast, specifically comparing Jacksonville and Savannah.' This forces the agent to use query_bls for granular, location-specific data, giving you the real picture.
Over-relying on basic web searches
Reading summary articles that provide a single, static number (e.g., 'Unemployment is 4.1%'). You miss the crucial trend data and historical context.
→
Use query_bls to pull the time-series data. This gives you the full chart of numbers—the dips, the spikes, and the consistent upward or downward trend—not just a single snapshot.
Asking for 'general economic insight'
Prompting, 'Tell me about the state of the labor market.' The agent gives a vague, non-actionable summary. You get fluff, not data.
→
Be specific. Use query_bls and specify the variables and regions: 'Give me the unemployment rate time series for the state of California and the last 10 years.' This forces a precise data pull.
When It Fits, When It Doesn't
Use this server if your analysis requires official, time-series labor statistics at the county, MSA, or state level. You need to compare 'A' to 'B' using hard numbers, not general trends.
Don't use it if you just need a quick, qualitative summary of the current job market or if you are only interested in one single city's single data point. For those cases, a general search or a simple lookup tool might suffice.
When you need to model economic divergence (e.g., comparing the recovery speed of two different states), this server is your only choice because of the query_bls tool's specialized, granular access.
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.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 1 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
You shouldn't have to manually jump between state and county data to build a regional picture.
Today, building a regional comparison means opening the BLS website, navigating to the LAUS program, and manually pulling reports for State A, then switching tabs to pull State B. You spend time cross-referencing dates and ensuring the data points are comparable, which is a huge waste of time.
With the BLS Local — LAUS MCP Server, your agent handles the whole process. You just ask for the comparison—say, 'How did unemployment shift between Miami-Dade and Cook County over five years?'—and the agent uses `query_bls` to pull the structured, comparable time-series data directly. You get the clean numbers, period.
BLS Local — LAUS MCP Server: query_bls
Before this, pulling data for multiple variables required knowing the exact BLS Series IDs and structuring complex, multi-step queries. It was a technical chore that slowed down the entire analysis.
Now, you specify the desired regions and variables in plain language. The agent translates that into the necessary `query_bls` calls, handling the complex API structure so you can focus on what the data means, not how to retrieve it.
Common Questions About BLS Local — LAUS MCP
How do I use the `query_bls` tool with the BLS Local — LAUS MCP Server? +
You prompt your agent with the specific variables, states, and timeframes you need. The agent translates this into the parameters required by query_bls, which fetches the structured time-series data.
Is the data from the BLS Local — LAUS MCP Server real-time? +
The data reflects the official BLS reports. While the server pulls data quickly, the underlying data is published on the BLS schedule and is not real-time minute-by-minute data.
Can the BLS Local — LAUS MCP Server compare different types of regions? +
Yes. You can compare states, specific counties, and Metropolitan Statistical Areas (MSAs) in the same query, letting you analyze diverse geographic areas side-by-side.
What specific data can I get using `query_bls`? +
The query_bls tool handles generic BLS v2 API timeseries queries. You must provide explicit BLS Series IDs, but it allows for pulling various labor metrics across regions.
What are the limitations and rate limits when using the `query_bls` tool? +
The query_bls tool allows up to 50 concurrent lookbacks. If you exceed this limit, your AI client will receive an error indicating the rate limit has been reached. You'll need to spread your queries out or optimize your data requests.
How do I handle specific BLS Series IDs when calling `query_bls`? +
You must provide explicit BLS Series IDs for the query_bls tool to work. These IDs are necessary because the tool queries the generic BLS v2 API timeseries endpoint. Always confirm the exact ID you need.
Does the `query_bls` tool support historical data lookups? +
Yes, the tool handles timeseries data, meaning it supports historical lookups. You simply need to specify the desired time range along with the required BLS Series IDs.
What kind of data structure does the `query_bls` tool return? +
The tool returns structured data containing the requested timeseries metrics. This data is ready for immediate analysis within your AI client, making it easy to process and visualize.
Does it list ALL US Counties? +
Yes, LAUS captures more than 3,100 specific county data-series actively every month.
How to get a key? +
Go to data.bls.gov/registrationEngine. It's totally free.
Can it run with Jobs data? +
Yes, merging state LAUS data with national CES nonfarm data provides an excellent contrast for macro reports.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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