How to Use the Cacheflow MCP in AutoGen

Create debating sales agents in AutoGen using Cacheflow to reach consensus on your proposals.

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Connect Cacheflow MCP to AutoGen

Create your Vinkius account to connect Cacheflow 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 Cacheflow with AutoGen

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Coordinate sales agents with Cacheflow

Assign `create_proposal` to your lead agent. Other agents in your group chat can then critique the proposal data before it gets finalized. This multi-agent setup forces your system to verify details. One agent drafts while the other checks for pricing accuracy.

Negotiate deal terms using AutoGen

Use `list_proposals` to pull current deal data into the conversation. Agents discuss the status and decide who needs to take action next. Conflict is good here. Your agents challenge each other's conclusions until they agree on the next step for the customer.

Streamline CRM updates in AutoGen

Trigger `sync_to_crm` only after your agents reach a consensus. This prevents accidental updates and ensures data integrity. Your agents act as a team. They confirm the deal is ready before pushing information into your sales database.

Setup guide

Set up Cacheflow 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 Cacheflow 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="Cacheflow_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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

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

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 Cacheflow. 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 Cacheflow MCP in AutoGen

Pass the tool list into your AssistantAgent constructor. The adapter handles the schema so your agents can start calling the server immediately.

Yes. You can grant access to the entire toolset or limit specific agents to read-only functions like `list_customers`.

It does. The tools are ready for any agent to invoke during their turn in the debate. The server handles the requests as they arrive.

Yes. Every tool call and its resulting JSON output are visible in the agent conversation history. You see exactly what the server provided.

All interactions pass through the Vinkius zero-trust gateway. Your data is restricted to the ephemeral session and never persists outside your authorized environment.

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