How to Use the USAJOBS (OPM) MCP in CrewAI

Q: What process should I use for bulk USAJOBS (OPM) data retrieval with CrewAI?

Use gethistoricjoas within an agent's task definition. The agent manages the continuation token logic, handling the high volume of archived listings.

Automate complex USAJOBS (OPM) research teams with CrewAI's specialized MCP Server agents.

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Connect USAJOBS (OPM) MCP to CrewAI

Create your Vinkius account to connect USAJOBS (OPM) 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.

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Setup USAJOBS (OPM) with CrewAI

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Researching Roles with the MCP Server

Assign a Research Agent to use `search_jobs` first. This agent finds initial USAJOBS (OPM) opportunities based on keywords and locations. The findings are stored in the shared memory for later analysis. A second Analysis Agent can then take those results and categorize them by job type or department.

Aggregating Job History Records

The Data Aggregation Agent uses `get_historic_joas` to pull bulk data. This agent automatically manages the pagination using continuation tokens, ensuring every archived record is collected and passed on. The collaboration structure makes sure no historical listing is missed.

Deep Review of Job Text for CrewAI

A Review Agent can pull a single job's details using `get_announcement_text`. It then passes the data to another agent that uses `get_code_list` to cross-check required skills against official codes. This validates the job requirements. The agents work together to provide complete, vetted information.

Setup guide

Set up USAJOBS (OPM) MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. 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 Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke USAJOBS (OPM) tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="USAJOBS (OPM) Analyst",
    goal="Access and analyze USAJOBS (OPM) data via MCP.",
    backstory="Expert analyst with direct USAJOBS (OPM) access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent USAJOBS (OPM) transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

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="USAJOBS (OPM) Analyst",
        goal="Access and analyze USAJOBS (OPM) data via MCP.",
        backstory="Expert analyst with direct USAJOBS (OPM) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent USAJOBS (OPM) transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by USAJOBS. 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 USAJOBS (OPM) MCP in CrewAI

You assign roles: one agent searches the jobs, another analyzes the findings, and a third writes the final report. They work sequentially or hierarchically.

Use `get_historic_joas` within an agent's task definition. The agent manages the continuation token logic, handling the high volume of archived listings.

Absolutely. You can assign a specialized Review Agent to pull text via `get_announcement_text` and then use the validation tool (`get_code_list`) in a subsequent step.

Give one agent the role of 'Monitor' watching all calls, while another takes the action using `search_jobs` and structured data retrieval.

This server touches job posting text, specifically long fields containing detailed professional descriptions. This is generally public-facing data.

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