How to Use the TaskForce MCP in AutoGen

Q: How do I use TaskForce with AutoGen for lead qualification?

Assign roles to your agents. One agent pulls leads using listtaskforceleads. A second reviews the details via gettaskforcelead and challenges the first agent's assumptions, forcing a more thorough, consensus-driven qualification process.

Q: What is the best way to model new service offerings with TaskForce and AutoGen?

Have agents debate the scope. One uses listtaskforcequotes to estimate costs; another reviews related cases (listtaskforcecases) for precedents. The final output is a risk-assessed, agreed-upon quote structure.

Get consensus decisions on sales strategy with AutoGen's TaskForce integration.

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Setup TaskForce with AutoGen

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Debating Optimal Lead Assignment

Set up an agent debate using `list_taskforce_leads`. One agent can argue for immediate assignment, while another reviews the lead context via `get_taskforce_lead` to flag required nurturing steps. The final consensus from your AutoGen system provides a vetted action plan, minimizing the risk of making quick, unvetted decisions.

Negotiating Service Scope and Quoting

Model complex negotiations. One agent uses `get_taskforce_customer` to understand history; another calls `list_taskforce_cases` for current needs. They debate the necessary scope, leading to a final decision that dictates which tools (like `list_taskforce_quotes`) should be run. This multi-agent setup ensures every angle—legal, financial, operational—is covered before proceeding.

Structuring New Opportunities and Cases

Use AutoGen to manage the creation of new records. One agent drafts the initial contact using `create_taskforce_lead`; a second reviews it against existing customer data via `get_taskforce_customer` for compliance risks. The agents then debate whether a formal case is needed, converging on calls like `create_taskforce_case` only when consensus is reached.

Setup guide

Set up TaskForce 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 TaskForce 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="TaskForce_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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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="TaskForce_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by TaskForce. 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 TaskForce MCP in AutoGen

Assign roles to your agents. One agent pulls leads using `list_taskforce_leads`. A second reviews the details via `get_taskforce_lead` and challenges the first agent's assumptions, forcing a more thorough, consensus-driven qualification process.

You absolutely can. Set up agents: one handles the query using `list_taskforce_invoices`, and another checks the associated customer record (`get_taskforce_customer`) for payment terms, debating the final necessary action.

Have agents debate the scope. One uses `list_taskforce_quotes` to estimate costs; another reviews related cases (`list_taskforce_cases`) for precedents. The final output is a risk-assessed, agreed-upon quote structure.

Yes. You build pipelines where agents must reach consensus on the next step—whether it's creating a record (`create_taskforce_case`) or simply retrieving data (`list_taskforce_customers`). It prevents unilateral, unchecked actions.

It touches lead details, customer records, case information, invoices, and quotes. AutoGen is ideal here because it forces agents to debate the sensitivity levels before accessing or modifying protected financial or personal identifiers.

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