How to Use the TaskForce MCP in CrewAI

Q: How does CrewAI help with TaskForce case management?

You assign roles: one agent lists existing cases (listtaskforcecases), and another creates new ones via createtaskforcecase. The agents pass the required information between them autonomously.

Q: What is the best way to get customer details with CrewAI and TaskForce?

The most direct path is to list all customers via listtaskforcecustomers, then have a second agent fetch specific data using gettaskforcecustomer for deep analysis.

Run autonomous sales operations with TaskForce using CrewAI's multi-agent framework.

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Connect TaskForce MCP to CrewAI

Create your Vinkius account to connect TaskForce to CrewAI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup TaskForce with CrewAI

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Coordinated Lead Intake and Assignment

You can assign a specialized 'Lead Agent' to manage incoming volume. This agent uses `list_taskforce_leads` to pull the latest data, then passes it off to an 'Analysis Agent' using `get_taskforce_lead` for context. The whole operation runs autonomously—no human needs to manually trigger each step; the agents collaborate until the task is done.

Full Customer Lifecycle Monitoring

Assign a 'Monitoring Agent' to track customer health. This agent first uses `list_taskforce_customers` and then passes the results to an 'Action Agent.' The action agent can then create records using `create_taskforce_case`. This mimics how a real sales team operates, with different specialists handling different parts of the client journey.

Financial Documentation Review

Set up a 'Finance Agent' that reviews deal status. This agent lists all quotes and invoices using `list_taskforce_quotes` and `list_taskforce_invoices`. It then gives the results to an overarching moderator agent. The result is an autonomous review of all financial data, which prevents human error when compiling a full sales package.

Setup guide

Set up TaskForce 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 TaskForce tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent TaskForce 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="TaskForce Analyst",
        goal="Access and analyze TaskForce data via MCP.",
        backstory="Expert analyst with direct TaskForce access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent TaskForce 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 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 CrewAI

You assign roles: one agent lists existing cases (`list_taskforce_cases`), and another creates new ones via `create_taskforce_case`. The agents pass the required information between them autonomously.

Yes. You can build specialized roles—a 'Scoring Agent' and a 'Creation Agent.' This allows for sophisticated, multi-step workflows that don't rely on manual intervention.

The most direct path is to list all customers via `list_taskforce_customers`, then have a second agent fetch specific data using `get_taskforce_customer` for deep analysis.

Yes, you pass the URL containing the TaskForce tools directly into your agents' configuration. The framework handles the selective tool exposure automatically.

The server touches leads, customers, cases, invoices, and quotes. It’s crucial that your agents only access and process the minimum necessary customer details.

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