Lightcast Labor Market MCP. Track skill gaps and regional demand data.
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Lightcast Labor Market connects your AI agent directly to global labor market data. It lets you track skill taxonomies (over 30k skills), standardize job titles, and pull regional economic summaries via natural conversation.
You can audit workforce health or map curricula against real-time industry demands.
What your AI agents can do
Get labor market region summary
Pulls a high-level summary of job volumes, median wages, and in-demand skills for a specific economic area.
Get lightcast api metadata
Retrieves status information about your Lightcast API connection setup.
Get skill details
Returns the full definition, scope, and specific context for any given skill taxonomy identifier.
Run get_labor_market_region_summary to get high-level data—like median wages and active job counts—for any specified economic area.
Use list_taxonomically_related_skills to find all technical or soft skills connected to a specific skill, helping identify adjacent career paths.
Trigger a quick audit with quick_labor_market_audit to get an instant summary of the current distribution of skills and occupations in your focus area.
List potential matches using list_standardized_job_titles or find official occupational codes with list_standardized_occupations, eliminating naming confusion.
Get full details and scope for any single skill (e.g., 'Python') using get_skill_details by its unique taxonomy identifier.
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Supported MCP Clients
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Lightcast Labor Market: 10 Tools for Workforce Analysis
These tools let you access and process global labor market data, allowing your AI client to perform detailed skill mapping, job title standardization, and regional economic analysis.
019d75c7get labor market region summary
Pulls a high-level summary of job volumes, median wages, and in-demand skills for a specific economic area.
019d75c7get lightcast api metadata
Retrieves status information about your Lightcast API connection setup.
019d75c7get skill details
Returns the full definition, scope, and specific context for any given skill taxonomy identifier.
019d75c7list economic regions
Lists all major geographical areas that Lightcast can pull labor market data from.
019d75c7list labor market skills
Retrieves a list of all available technical and soft skills found within the entire Lightcast taxonomy.
019d75c7list skill taxonomic categories
Lists the high-level groups (e.g., 'Programming,' 'Communication') that organize the skill data.
019d75c7list standardized job titles
Provides a list of standardized, accepted job titles used across industry reports.
019d75c7list standardized occupations
Lists official occupations recognized by Lightcast (compatible with SOC/O*NET standards).
019d75c7list taxonomically related skills
Identifies and lists skills that are logically related to a primary skill, suggesting adjacent proficiencies.
019d75c7quick labor market audit
Generates an instant summary of the overall distribution of skills and occupations across the entire index.
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What you can do with this MCP connector
Lightcast Labor Market MCP Server - Skill & Job Data
Your AI agent connects directly to Lightcast's massive labor market database. It lets you treat job skills, industry titles, and regional economies like a single data source. You won't just read reports; your agent runs queries against 30,000+ skills and standardized job codes, giving you actionable intelligence on workforce demands.
Regional Market Analysis
- To check the overall health of an economy, run
get_labor_market_region_summary. You'll pull high-level data for any specified area—things like median wages, current job volumes, and what skills are hot right now. You first uselist_economic_regionsto see every major geographical zone Lightcast covers before running that summary. - The system knows official occupations through
list_standardized_occupations, listing codes compatible with SOC/O*NET standards so you never confuse a job title with an actual classification. - To keep track of what data's available, your agent can pull the API setup status using
get_lightcast_api_metadata.
Skill Taxonomy and Mapping
- The server structures skills into high-level groups; use
list_skill_taxonomic_categoriesto see these major buckets (like 'Programming' or 'Communication'). You can then pull a comprehensive list of every technical and soft skill found in the entire system usinglist_labor_market_skills. - If you need deep context, run
get_skill_detailswith any unique taxonomy ID. This gives you the full definition and specific scope for that single skill. - You can map adjacent proficiencies by running
list_taxonomically_related_skills. This identifies other skills logically tied to a main skill, helping you trace potential career paths.
Job Title Standardization & Auditing
- Naming conventions are messy. To eliminate confusion across reports, use
list_standardized_job_titlesto get accepted job titles that match industry reporting standards. - To see the current distribution of skills and jobs across your entire focus area, run a quick audit with
quick_labor_market_audit. It generates an instant summary index-wide.
This setup handles everything from finding out what's hot in Wages/Skills for a specific zone to getting the precise definition for 'React' or verifying if 'UX Designer' matches a formal occupational code. Your agent does the heavy lifting, keeping your workflow focused on insights.
How Lightcast Labor Market MCP Works
- 1 First, list the economic regions you want to analyze using
list_economic_regions. This defines your scope. - 2 Next, run
get_labor_market_region_summaryfor that region. The tool returns a data payload containing wage trends and job volumes. - 3 Finally, cross-reference specific skill gaps by listing categories (
list_skill_taxonomic_categories) and then pulling detailed definitions usingget_skill_details.
The bottom line is that you feed the AI client a structured query (e.g., 'Compare skills in Region A vs. Region B'), and it executes multiple tools to synthesize the answer for you.
Who Is Lightcast Labor Market MCP For?
This is for corporate strategists, HR leaders, and academic researchers who need more than just internal data dumps. If your job involves predicting staffing needs or aligning educational curricula with market reality, this saves weeks of manual research.
Needs to quickly check if a candidate's stated skill (e.g., 'backend dev') matches standardized titles using list_standardized_job_titles before posting a job.
Must monitor regional shifts, running get_labor_market_region_summary to track how wage trends in Austin compare to Denver quarter over quarter.
Needs to audit an entire degree program by cross-referencing its required courses against the official taxonomy using list_skill_taxonomic_categories and get_skill_details.
What Changes When You Connect
- Pinpoint real-time workforce needs. Instead of guessing, you run
get_labor_market_region_summaryto get actual job volumes and median wages for any area. This keeps your projections grounded in reality. - Standardize everything from skill to title. Stop dealing with synonyms or regional variations. Use
list_standardized_job_titlesfirst, then cross-reference the role against official codes vialist_standardized_occupations. It eliminates ambiguity instantly. - Map curriculum requirements precisely. Need to validate a degree program? Start by calling
list_skill_taxonomic_categories, then drill down withget_skill_detailsto ensure every module maps correctly to an industry-recognized skill. - Audit market health in one go. Don't run ten separate reports. A single call to
quick_labor_market_auditgives you a high-level summary of the entire skill and occupation index, giving you a starting baseline for any analysis. - Understand adjacent skills. If your client is strong in 'Python,' don't forget related fields. Use
list_taxonomically_related_skillsto pull out Machine Learning or SQL—the next logical step for their career.
Real-World Use Cases
Launching a new regional office
The problem: You're moving to Miami, but don't know what roles are hot. Your agent runs list_economic_regions to confirm 'Miami-Dade'. Then it hits get_labor_market_region_summary. Result: It shows 1.5 million active jobs and flags 'Cybersecurity' as a top skill gap, telling you exactly where to focus hiring efforts.
Updating the company job catalog
The problem: Your team uses five different internal names for 'Full Stack Developer.' Instead of manually reconciling them, your agent runs list_standardized_job_titles and compares it against list_standardized_occupations. Result: You get three official codes and the correct standardized title to use everywhere.
Designing a new tech bootcamp
The problem: Your education team needs curriculum validation. They start by asking for all 'Data Science' related skills using list_taxonomically_related_skills. Then, they check the official definition of each skill using get_skill_details to ensure the depth and scope are correct.
General market sanity check
The problem: Leadership wants a quick snapshot of what skills matter right now without picking a region. Your agent runs quick_labor_market_audit. Result: It spits out a summary showing the general distribution across all indexed skills and occupations, giving instant context.
The Tradeoffs
Assuming skill overlap
The user asks their agent to 'find all data science jobs' without specifying a region or using the proper tools. The agent returns general, uncontextualized noise.
→
Always scope your query first. Run list_economic_regions to pick a boundary (e.g., 'Northeast'). Then use that region ID in get_labor_market_region_summary and cross-reference the key skills found with list_skill_taxonomic_categories. This locks down the context.
Mixing up job titles and occupations
A user asks for a list of 'jobs' but doesn't know if Lightcast means standardized titles or official codes. They get inconsistent results.
→
Use two separate tools: list_standardized_job_titles gives the common, readable names (e.g., 'Engineer'). Use list_standardized_occupations when you need the strict, code-compatible industry classification (like SOC).
Overlooking skill relationships
The user focuses only on a primary skill like 'Java' but misses adjacent technologies that are currently in high demand.
→
Once you identify a core skill, run list_taxonomically_related_skills. This pulls the surrounding context—like related languages or frameworks—that your competitors might be using right now.
When It Fits, When It Doesn't
Use this server if your work hinges on quantifying labor market realities. If you need to measure skill supply vs. demand, check regional salary trends, or validate educational curricula against industry standards, this is necessary. It’s for systemic workforce planning.
Don't use it if you just want simple definitions. If you only need the definition of 'Agile Methodology,' running get_skill_details is overkill; a standard dictionary lookup works fine. Furthermore, don't use it to predict market shifts based on political rumors—the data reflects existing economic activity, not future hypotheticals. Always confirm your scope using list_economic_regions first. If you only care about job titles and nothing else, you could just use a dedicated job board API instead of this full labor market toolset.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Lightcast Labor Market. 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 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manual market research takes forever.
Today, figuring out what skills are actually hot in the Chicago tech scene means opening five different reports. You copy a skill name from one document, paste it into another to see if it matches an official code, and then you manually cross-check wage data for that region. It's clicks, tabs, and hours of comparison.
With this MCP server, the agent does the heavy lifting. You just tell it: 'What are the top 5 skills in Chicago and what are their median wages?' It runs `get_labor_market_region_summary`, pulls the skill data using `list_skill_taxonomic_categories`, and gives you a single, structured answer. Done.
Lightcast Labor Market MCP Server: Get standardized job titles.
Before this integration, if your company called someone an 'AI Specialist,' but the government report used 'Machine Learning Engineer' and another report used 'Data Scientist II,' you had to manually reconcile all three names every time. It was a constant headache of synonyms.
Now, running `list_standardized_job_titles` solves that. You get a definitive list of industry-accepted titles. You don't argue about what the job is called; your agent just tells you the standard name.
Common Questions About Lightcast Labor Market MCP
What does `get_labor_market_region_summary` pull? +
It pulls a summary of the local labor market, including active job counts and median advertised wages. You specify an economic region (e.g., 'Boston') to get these metrics.
How do I find related skills using `list_taxonomically_related_skills`? +
Give the tool a primary skill ID or name, and it returns a list of other skills that are logically connected. This helps you map adjacent career paths.
Do I need to use `list_economic_regions` before running any summary? +
Yes. You must first call list_economic_regions to get the list of valid geographic codes. The server needs this scope to run reports accurately.
What's the difference between job titles and occupations? (using `list_standardized_job_titles` vs `list_standardized_occupations`) +
Job titles (list_standardized_job_titles) are common, readable names used in hiring. Occupations (list_standardized_occupations) are the official, code-based classifications (like SOC).
What does the `get_lightcast_api_metadata` tool confirm about my connection? +
It validates your API access credentials and reports the current status of your Lightcast connection. This is critical for troubleshooting before running any large reports, confirming that your client ID and secret are properly set up.
If I need the full definition for a specific skill, how do I use `get_skill_details`? +
The function returns a comprehensive record including definitions, usage codes, and metadata for that single skill. This goes beyond just knowing the name; you get the official taxonomy structure required for accurate reporting.
Is running `quick_labor_market_audit` faster than listing all skills using `list_labor_market_skills`? +
Yes, it pulls a high-level index snapshot designed for instant analysis. It aggregates skill and occupation volume data immediately without needing to loop through thousands of individual entries.
If I need to map skills into broad groups, how does `list_skill_taxonomic_categories` help? +
It provides the top-level structural framework for every skill within the taxonomy. This is essential when your agent needs to filter or categorize data before analyzing specific requirements.
How do I get Lightcast API credentials? +
You need a registered developer account at the Lightcast Developer Portal. Once approved, you can create an application to receive your Client ID and Client Secret.
Which API versions does this support? +
This integration currently utilizes the latest stable versions of the Skills, Titles, and Occupations APIs provided by the Lightcast platform.
Is global labor data available? +
Yes, Lightcast provides labor market data for various international regions. Availability depends on the specific geographic permissions associated with your API credentials.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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