How to Use the Kippy MCP in CrewAI

Deploy a specialized crew of AI agents to monitor Kippy performance metrics and audit logs autonomously.

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Connect Kippy MCP to CrewAI

Create your Vinkius account to connect Kippy 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 Kippy with CrewAI

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Assign Specialized Analyst Roles

The Kippy MCP Server turns your CrewAI setup into an automated HR department. You assign `list_team_scores` and `list_project_scores` to a dedicated analyst agent. That specific worker spends its time crunching the numbers. Meanwhile, a separate reporting agent takes those findings and drafts an executive summary. They share memory and pass context back and forth without human intervention.

Automate System Surveillance

System compliance requires constant surveillance. You give a monitor agent access to `list_audit_logs`. It runs on a schedule, watching for unauthorized access patterns. The MCP Server feeds raw logs into your pipeline. If the agent spots an anomaly, it escalates the issue to a moderator agent. The hierarchy dictates exactly how the crew responds to potential security events.

Generate Quarterly Reviews

Generating quarterly reviews takes days of manual work. You feed `list_appraisals` and `list_feedback` into a CrewAI sequential pipeline. The agents read the historical data first. They cross-reference peer notes with actual project outcomes. The final output is a drafted performance review, fully cited from the underlying corporate database.

Setup guide

Set up Kippy 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 Kippy tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

You supply the endpoint URL directly in the mcps array when defining your agent. The framework handles the tool registration automatically.

You use MCPServerHTTP from the crewai.mcp module. Pass a tool_filter to ensure an agent only sees the exact endpoints it needs for its role.

Agents share a unified memory context. The researcher pulls the project data once, and the writer agent references those exact figures in the final report.

The Python client connects via standard input/output, Server-Sent Events, or Streamable HTTP. You pick the transport that fits your deployment environment.

Raw KPI scores define compensation and bonus structures. You isolate this data by restricting the list_kpi_scores tool to a single, highly-privileged agent within your crew hierarchy.

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