How to Use the JobProgress (Leap) MCP in CrewAI

Coordinate autonomous field agent teams for CrewAI with this MCP Server.

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Connect JobProgress (Leap) MCP to CrewAI

Create your Vinkius account to connect JobProgress (Leap) 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 JobProgress (Leap) with CrewAI

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Autonomous field coordination in CrewAI

Deploy a specialized crew where one agent manages the calendar via `list_appointments` and another tracks project health using `list_jobs`. They share information to keep the whole operation moving. This setup allows agents to collaborate without your constant input. They cross-reference `get_customer` data to ensure the right contact is assigned to the right project.

Specialized agent roles for project monitoring

Assign specific responsibilities to your agents based on the tools they use. A monitoring agent can watch `list_workflows` for delays, while a response agent handles the updates. By delegating tasks this way, your CrewAI setup becomes highly efficient. Each agent has a focused view of the data, reducing the chance of missed updates or scheduling conflicts.

Shared memory for field operations

Enable your agents to remember context from previous runs using shared memory. If an agent finds an issue in `list_tasks`, the next agent in the sequence knows exactly what to address. This creates a cohesive operation where agents act as a unified team. They handle the routine checks while you only intervene for high-level decisions.

Setup guide

Set up JobProgress (Leap) 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 JobProgress (Leap) tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent JobProgress (Leap) 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 →

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

    task = Task(
        description="List recent JobProgress (Leap) 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 JobProgress. 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 JobProgress (Leap) MCP in CrewAI

Pass the server URL into the agent's MCP configuration. The framework handles the rest, exposing the tools to your chosen agents.

Yes, the agents can access the shared memory pool. This allows them to pass information about customers and jobs between one another seamlessly.

It provides your agents with a direct line to your business data. They spend less time asking for updates and more time executing on their assigned tasks.

The server is optimized for high-uptime connections. It maintains the link to your agents throughout the entire duration of their assigned work.

We use ephemeral transport for all data transfers. Your customer contact details and internal project metadata remain in memory only, protected by our zero-trust architecture.

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