How to Use the Range MCP in AutoGen

Q: How do AutoGen agents handle conflicting Range updates?

The agents use listupdates to compare different check-ins and run a debate loop to reconcile the statuses before updating any goals.

Q: What is the best way to inspect a single team's progress?

Call getteam to fetch the team's membership and current focus, then use that team ID to filter updates and objectives.

Let AutoGen agents debate team blockers and draft async updates using Range tools without scheduling more meetings.

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Setup Range with AutoGen

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Debating team blockers with AutoGen

This MCP Server gives your AutoGen agents the tools to pull updates using `list_updates` and discuss potential resolutions. One AutoGen agent can analyze a blocker from `get_snippet` while a coordinator agent checks team capacity before agreeing on a solution.

Drafting daily updates through consensus

This MCP Server automates daily updates by letting AutoGen agents collaborate on drafts. Set up an AutoGen writing agent that pulls your git commits and matches them against active targets using `list_goals` before a separate reviewer agent calls `create_update` to post the final draft.

Aligning cross-team objectives

This MCP Server aligns cross-team objectives inside your AutoGen conversation. Your AutoGen agents call `list_objectives` to spot discrepancies across different departments and use `get_team` to negotiate ownership of shared tasks in their chat history.

Setup guide

Set up Range 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 Range 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="Range_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Range. 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 Range MCP in AutoGen

Use the server tools utility from the AutoGen extension package to pass the HTTP transport parameters to your agent constructor.

Yes. Since the server is hosted in a secure, stateless sandbox, different agents can call `get_update` or `list_goals` at the same time to share context during their debates.

The agents use `list_updates` to compare different check-ins and run a debate loop to reconcile the statuses before updating any goals.

Call `get_team` to fetch the team's membership and current focus, then use that team ID to filter updates and objectives.

Yes. Your private updates, user lists, and team goals run within an isolated, zero-trust sandbox environment with ephemeral execution.

Use it with your favorite AI tools

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