How to Use the Onpipeline MCP in CrewAI

Deploy autonomous sales crews to manage your Onpipeline MCP Server with specialized agents in CrewAI.

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Connect Onpipeline MCP to CrewAI

Create your Vinkius account to connect Onpipeline 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 Onpipeline with CrewAI

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Analyze Pipelines with Autonomous Crews

`list_pipelines` and `list_crm_deals` feed raw sales data to your CrewAI research agent. This specialized worker scans the active deals to identify stalled negotiations. It passes its findings to an analyst agent for deeper review. Role-based execution keeps the logic clean. The researcher only reads data, while a separate manager agent decides if an intervention is necessary based on the shared memory context.

Delegate Data Entry to AI Workers

`create_crm_deal` and `create_crm_contact` execute CRM updates without human intervention. A data-entry agent monitors a shared inbox, extracts lead information, and populates the database. It structures the payload exactly as the API requires. You define the hierarchy. A moderator agent reviews the proposed records in memory before allowing the actor agent to commit the transaction to the database.

Onpipeline MCP Server Event Management

`list_activities` and `list_crm_events` expose the sales team's calendar to your autonomous crew. The system checks for scheduling conflicts or missed follow-ups. It flags accounts that haven't received attention in the last 14 days. The crew operates continuously in the background. You set the execution strategy to sequential, ensuring the audit finishes completely before any alert messages get generated.

Setup guide

Set up Onpipeline 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 Onpipeline tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent Onpipeline 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 Onpipeline. 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 Onpipeline MCP in CrewAI

Pass the server URL directly into the mcps array parameter of your Agent definition. You will need the crewai[tools] package installed via pip.

Absolutely. Use MCPServerHTTP from crewai.mcp and apply a tool_filter. This restricts a reading agent to only see list commands.

The framework maintains a shared memory state. One agent queries get_deal_details, and the resulting data becomes available to the next agent in the sequence.

The framework supports stdio, SSE, and Streamable HTTP. Vinkius defaults to HTTP for managed connections, which CrewAI handles natively.

Meeting times and participant lists stay protected. The MCP Server executes in an ephemeral sandbox that destroys itself after the run. Vinkius never stores your scheduling data on disk.

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