How to Use the JobProgress (Leap) MCP in AutoGen

Q: How does the server protect my JobProgress (Leap) scheduling data in AutoGen?

All scheduling information retrieved via listappointments is routed through a zero-trust V8 isolate sandbox managed by this MCP Server. Your operational calendars are never cached or exposed to external networks.

Deploy AutoGen multi-agent squads to debate, verify, and track your JobProgress (Leap) field operations.

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Connect JobProgress (Leap) MCP to AutoGen

Create your Vinkius account to connect JobProgress (Leap) to AutoGen 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 AutoGen

Ask AI about this MCP

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Perplexity

Consensus-driven project tracking

`list_jobs` provides the baseline project status for your multi-agent conversation. In AutoGen, one agent can review job progress while a scheduling agent uses `list_appointments` to verify if the field crew is on track. They debate discrepancies. If the job status is marked complete but appointments are still pending, the agents flag the mismatch. Run the `get_job` call first to confirm the exact workflow stage.

Multi-agent task auditing via MCP Server

`list_tasks` retrieves active to-do lists across your entire organization. An AutoGen auditor agent analyzes these tasks, while a resource agent pulls active team members using `list_users`. They negotiate task reassignments based on workload. Instead of a single model making a guess, the agents deliberate to balance the schedule before presenting the final list to your manager.

Proposal and estimate verification

`list_proposals` gets the current contract details, and `list_estimates` brings in the financial breakdowns. A financial agent and a sales agent analyze these records side by side. They cross-reference the numbers to ensure margins are protected. The agents flag low-margin proposals before they go out to the customer, saving your business from costly pricing errors.

Setup guide

Set up JobProgress (Leap) MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes JobProgress (Leap) tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="JobProgress (Leap)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent JobProgress (Leap) data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="JobProgress (Leap)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent JobProgress (Leap) data")
    print(result.messages[-1].content)

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 AutoGen

Use the MCP tool adapter with your Vinkius server URL. Pass the adapted tools directly into your AssistantAgent constructor to make them available for agent conversations.

Yes. The tools are registered at the conversational level, allowing different agents in your group chat to call `list_jobs` or `get_customer` as needed during their debate.

The extension's tool adapter automatically converts the MCP tool schemas into the format required by AutoGen, so your agents can invoke them without custom mapping.

Yes. You can configure the connection using either stdio or Streamable HTTP transports depending on how you host your multi-agent system.

All scheduling information retrieved via `list_appointments` is routed through a zero-trust V8 isolate sandbox managed by this MCP Server. Your operational calendars are never cached or exposed to external networks.

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