How to Use the BLS Wages — OEWS Occupational Employment MCP in CrewAI
Deploy a specialized crew of HR agents using CrewAI and official BLS wage data.
Works with every AI agent you already use
…and any MCP-compatible client
Connect BLS Wages — OEWS Occupational Employment MCP to CrewAI
Create your Vinkius account to connect BLS Wages — OEWS Occupational Employment to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.
Run multi-agent wage analysis in CrewAI
Assign the `query_bls` tool to a specialized research agent in CrewAI to fetch raw occupational statistics automatically. CrewAI shines when you set up specialized teams where one agent gathers the raw numbers and another analyzes the trends. This collaborative setup prevents your model from getting overwhelmed. Each agent focuses on a single task, resulting in highly accurate salary reports without manual intervention.
Expose specific tools to your agent crew
Restrict the `query_bls` tool to specific agents in your crew using `MCPServerHTTP` to control your API usage. You don't always want every agent in your crew hitting this MCP server. Filtering your tools ensures your agents don't waste time running redundant queries. It keeps your token usage down and your execution paths clean.
Autonomous salary benchmarking pipelines
Automate your salary benchmarking pipeline by letting your crew trigger `query_bls` whenever a new hiring request comes in. Your CrewAI MCP setup can monitor incoming roles and fetch the latest state-level median earnings instantly. The entire process runs in the background, combining government data with your internal metrics to keep your hiring pipeline moving fast without human intervention.
Set up BLS Wages — OEWS Occupational Employment MCP in CrewAI
Prerequisites
- Python 3.10+ installed
-
crewaipackage (pip install crewai) - Active Vinkius subscription with a valid endpoint token
- 1
Install CrewAI
Run
pip install crewaito install the framework. MCP support is built-in via themcpsparameter. - 2
Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the
mcpslist. Replace[YOUR_TOKEN_HERE]with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically. - 3
Kick off your crew
Create a
Crewwith your agent and tasks. Callcrew.kickoff()— the agent will automatically invoke BLS Wages — OEWS Occupational Employment tools as needed.
from crewai import Agent, Task, Crew
agent = Agent(
role="BLS Wages — OEWS Occupational Employment Analyst",
goal="Access and analyze BLS Wages — OEWS Occupational Employment data via MCP.",
backstory="Expert analyst with direct BLS Wages — OEWS Occupational Employment access.",
mcps=[
"https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
],
)
task = Task(
description="List recent BLS Wages — OEWS Occupational Employment transactions",
agent=agent,
expected_output="A summary of recent activity",
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)Prerequisites
- Python 3.10+ installed
-
crewai+crewai-toolspackages - Active Vinkius subscription with a valid endpoint token
- 1
Install dependencies
Run
pip install crewai crewai-tools. TheMCPServerAdapterhandles lifecycle management and tool conversion. - 2
Connect with MCPServerAdapter
Use
MCPServerAdapteras a context manager withSseServerParameterspointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle. - 3
Assign tools and run
Pass the returned
mcp_toolsto your agent'stoolsparameter. The adapter converts MCP tools to nativeBaseToolobjects compatible with all CrewAI agents.
from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters
server_params = SseServerParameters(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
with MCPServerAdapter(server_params) as mcp_tools:
agent = Agent(
role="BLS Wages — OEWS Occupational Employment Analyst",
goal="Access and analyze BLS Wages — OEWS Occupational Employment data via MCP.",
backstory="Expert analyst with direct BLS Wages — OEWS Occupational Employment access.",
tools=mcp_tools,
)
task = Task(
description="List recent BLS Wages — OEWS Occupational Employment transactions",
agent=agent,
expected_output="A summary of recent activity",
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)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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Common questions about BLS Wages — OEWS Occupational Employment MCP in CrewAI
Use it with your favorite AI tools
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