BLS Wages MCP. Compare median pay for any job and state.
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query_bls is the BLS Wages — OEWS Occupational Employment MCP Server tool. Use it to pull specific wage data. You can compare median and average earnings across hundreds of distinct professions and multiple states.
It lets you track true wage distributions by profession and location, making it the go-to source for labor market salary data.
What your AI agents can do
Query bls
Runs a generic, timeseries query for BLS data, requiring explicit BLS Series IDs. Allows up to 50 concurrent lookbacks.
It pulls median and average pay data for a given job title when comparing two or more different states.
It provides data points ranging from the 10th to the 90th percentile for salary benchmarking.
It retrieves wage data using explicit BLS Series IDs and standardized occupation codes.
It calculates average hourly boundaries for specific professions at a national or state level.
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BLS Wages MCP Server: 1 Tool for Wage Data Query
Use the query_bls tool to run generic, timeseries queries against the BLS data, retrieving historical median earnings for specific professions and states.
019d755fquery bls
Runs a generic, timeseries query for BLS data, requiring explicit BLS Series IDs. Allows up to 50 concurrent lookbacks.
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What you can do with this MCP connector
You'll use the query_bls tool to get specific wage data from the Bureau of Labor Statistics. You can compare median and average pay for a job title across multiple states. This tool lets you analyze true wage distributions by providing salary benchmarks from the 10th to the 90th percentile. You'll pull data for specific professions using explicit BLS Series IDs and standardized occupation codes.
You can also determine average hourly boundaries for any profession, whether you look at a whole state or just one. The query_bls tool runs a generic, timeseries query for BLS data and handles up to 50 concurrent lookbacks.
How BLS Wages MCP Works
- 1 Tell your agent the specific job roles, states, and time period you want to compare.
- 2 The agent runs the
query_blstool, sending the necessary BLS Series IDs and criteria. - 3 You get a data table comparing median and average earnings for the requested professions and locations.
The bottom line is you get precise wage comparisons across professions and states directly from official BLS data.
Who Is BLS Wages MCP For?
Anyone who needs to know what a job pays for sure. This is for Compensation Analysts, HR Strategists, Market Researchers, and Recruiters. If your job involves benchmarking salaries or analyzing regional pay disparities, you need this.
They use the tool to validate internal salary bands against external market data, ensuring pay equity across different states.
They pull data to advise clients on the cost of labor for specific industries in different geographic regions.
They check regional pay gaps for high-demand roles (e.g., Software Developers) to adjust job postings and offer letters.
What Changes When You Connect
- Pinpoint exact salary gaps. Instead of guessing, you get precise median wage distributions for roles like 'Registered Nurse' in California versus 'Texas' using the
query_blstool. - Benchmark pay percentiles. You can compare top-tier salaries (90th percentile) against entry-level pay (10th percentile) for roles like Accountants, giving you a full picture of wage stratification.
- Track hourly rates nationally. Get the average hourly wage for professions like Registered Nurses across the country, seeing where pay rates spike in coastal markets.
- Compare state-to-state pay. Instantly see how the median wage for a Software Developer changes when comparing two major states, saving manual research time.
- Query specific data series. Use the
query_blstool with explicit BLS Series IDs to pull historical or niche economic data not easily found through general searches.
Real-World Use Cases
Determining pay parity for high-demand roles
A TA Manager needs to know if their pay range for Software Developers is competitive in both New York and Oregon. They ask their agent to run the query_bls tool, specifying the job title and the two states. The agent returns a direct comparison of median wages, allowing the manager to adjust the salary band immediately.
Building a labor cost model
A Market Researcher is building a cost model for opening a new branch. They need to compare the average wage of specialized Accountants across four different states. They instruct the agent to run the query_bls tool with the necessary codes and state inputs, generating a structured comparison table for the business plan.
Analyzing career pay trajectory
An HR Strategist wants to show employees the pay difference between entry-level and executive roles. They ask the agent to run the query_bls tool comparing the 10th percentile to the 90th percentile for a profession like Accountants, providing hard data for retention talks.
Calculating national hourly averages
A payroll specialist needs a quick check on the average hourly rate for Registered Nurses across the country. They prompt the agent to query the national average hourly boundary using the query_bls tool. The agent returns a precise, current average, confirming the payroll estimate.
The Tradeoffs
Guessing the data point
Searching 'How much does a nurse make in Texas?' and getting a vague range from general search results. This forces manual cross-referencing and is often outdated.
→
Use the query_bls tool. You must specify the job title, the state, and the desired metric (median/average) to get data directly from the source.
Over-relying on single-source data
Using only job board data for salary comparisons. These sites often show ideal or inflated pay, not the true median wage.
→
Use the query_bls tool. It pulls official median earnings from the Bureau of Labor Statistics, giving you a more accurate, benchmarked number.
Ignoring the specific code structure
Trying to ask the agent to compare 'Tech Workers' generally. The tool requires specific occupation codes or names for accurate querying.
→
Use the query_bls tool and provide the specific BLS Series IDs. The tool is built for precision, not for general concepts.
When It Fits, When It Doesn't
Use this server if your primary need is objective wage benchmarking. If you need to compare the median or average pay for specific professions (like Software Developers) across multiple states, this tool is essential. It works when you know you need BLS data.
Don't use this if you're looking for anecdotal salary discussions, or if you need real-time job posting sentiment analysis. For that, you'd need a different, job-board scraping tool. This server is for established, official labor statistics, period.
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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This server provides 1 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manual salary research takes too much time.
Right now, figuring out if a job in Seattle pays better than one in Miami requires jumping between state government sites, salary aggregator websites, and outdated job board listings. You spend hours cross-checking data, often ending up with vague ranges or needing to guess which year's data is correct.
With the BLS Wages MCP Server, you ask your agent for a comparison—say, Software Developers in Seattle vs. Miami. The agent runs the `query_bls` tool and returns a structured comparison of median wages instantly. You get the definitive data point you need, period.
BLS Wages MCP Server: Query median wages with `query_bls`
You eliminate the need to visit the BLS website and manually construct complex queries. You don't have to remember which BLS Series ID corresponds to 'Registered Nurse' or which format to use for state comparisons.
Your AI client handles the data plumbing. You state the problem—'Compare Nurse pay in TX vs IL'—and the agent uses `query_bls` to deliver the structured, reliable answer. It's that simple.
Common Questions About BLS Wages MCP
How do I use the `query_bls` tool for salary comparisons? +
You must tell your agent to compare specific roles and states. For example: 'Compare median salary for Software Developers in California versus Texas.' The agent runs query_bls and formats the results.
Is the data from the BLS Wages MCP Server up-to-date? +
The data reflects official Bureau of Labor Statistics records. Always remember that official statistics have a reporting lag; the data is authoritative but not always real-time.
What kind of data does `query_bls` handle? +
The query_bls tool handles general BLS time-series queries. It requires explicit BLS Series IDs and is best used for standardized wage and occupational data.
Can I compare multiple jobs in one query? +
Yes. You can prompt your agent to compare several distinct professions and states simultaneously, allowing you to build comprehensive salary matrices.
What are the requirements for using the `query_bls` tool, and does it handle historical data? +
The query_bls tool requires explicit BLS Series IDs. You can run historical queries because the API supports time-series data lookbacks.
If I need to check a very large number of salary comparisons, what are the limits of the `query_bls` tool? +
The query_bls tool allows up to 50 concurrent lookbacks. If you exceed this, you'll need to break your request into smaller batches.
Does `query_bls` accept input beyond just state and job titles, or does it require specific codes? +
The tool requires explicit BLS Series IDs. You must use these numerical codes rather than natural language job descriptions for the most accurate results.
What happens if I run a query that doesn't match any known BLS codes using `query_bls`? +
The tool will return an error indicating invalid or non-existent Series IDs. This signals that the requested data combination is not available in the BLS dataset.
How reliable is OEWS compared to private job boards? +
Extremely. OEWS pulls directly from true tax and payroll disclosures to the government, eliminating inflated self-reported figures typical on private job sites.
Is a Key required? +
Only one single Key is required from the registration page. Simply plug it into the settings page and access the entire 20-year catalog of wage distribution profiles globally.
Why is wage data a unique server? +
Because querying compensation brackets specific to states and detailed codes (like separating Senior vs Junior codes horizontally) takes specialized tool structures optimally tailored here.
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