How to Use the Kippy MCP in AutoGen

Q: Can my AutoGen agents debate Kippy performance reviews?

Absolutely. You can assign one agent to analyze listappraisals and another to debate the findings until they reach a consensus.

Debate your team performance metrics using AutoGen agents connected to the Kippy MCP server.

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Connect Kippy MCP to AutoGen

Create your Vinkius account to connect Kippy 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 Kippy with AutoGen

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Perplexity

Consensus-driven team analysis

Let your agents debate the meaning of `list_team_scores`. One agent flags a dip, while another challenges the underlying project data. This setup forces your system to reconcile different viewpoints. You get a decision based on multiple agents reviewing `list_projects` in parallel.

Automated performance negotiation

Agents negotiate project timelines using `list_kpi_scores`. They verify constraints and push for realistic targets based on actual data. You avoid biased reporting. The agents talk until they agree on the status of `list_project_scores`, providing you with a single, vetted conclusion.

Agentic oversight of audit logs

A security agent monitors `list_audit_logs` for anomalies. It reports directly to your management agent for review. You build a system that talks to itself to keep your records clean. The agents ensure `list_users` access is always within expected parameters.

Setup guide

Set up Kippy 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 Kippy 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="Kippy_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Kippy 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="Kippy_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Get your connection token →

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Common questions about Kippy MCP in AutoGen

Install the autogen-ext package and pass your server tools to the AssistantAgent. The adapter handles the schema conversion for you.

Absolutely. You can assign one agent to analyze `list_appraisals` and another to debate the findings until they reach a consensus.

The MCP server runs in a Vinkius sandbox. Your agents only see the data they request through the tool calls you explicitly authorize.

Yes. Use the StreamableHttpServerParams to connect your agents. It supports both stdio and HTTP transports for maximum flexibility.

You can wrap the tools in custom agent functions. This lets you pre-process or filter `list_notes` before the agent even sees them.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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