How to Use the GitScrum Sprints MCP in AutoGen

Q: How do I configure GitScrum Sprints in AutoGen?

Install autogen-extmcp and import mcpservertools with your Vinkius HTTP endpoint. Configure this MCP server by passing the generated tools list directly into your AssistantAgent constructor.

Q: Can AutoGen agents create epics in GitScrum Sprints?

Yes, agents use createuserstory and listepics to build out project hierarchies. They discuss the requirements in their chat channel before executing the tool calls.

Q: How do AutoGen agents handle conflicting GitScrum Sprints data?

If sprintprogress shows a lag but sprintmetrics suggests high velocity, your agents will debate the discrepancy. They query listtasks to find the exact bottleneck before reaching a consensus.

Let AutoGen agents debate sprint scope and velocity using live GitScrum Sprints data to optimize planning.

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Create your Vinkius account to connect GitScrum Sprints 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 GitScrum Sprints with AutoGen

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Run Multi-Agent Debates on Sprint Goals

This MCP server lets your AutoGen agents analyze sprint capacity using `sprint_kpis` and `sprint_metrics` simultaneously. A product manager agent can propose a high-scope sprint, while a developer agent uses live velocity data to challenge the plan. They negotiate the final scope before calling `create_sprint` to lock in the commitment. This consensus-driven approach ensures your sprint planning is grounded in historical team performance.

Automate Ticket Triage with AutoGen MCP Server

The `list_tasks` and `get_task` tools provide the raw data your agents need to categorize incoming work. One agent filters tasks by status, while another evaluates the complexity of the user stories using `list_user_stories`. Once they agree on the priority, they use `update_sprint` to assign the work. You get an automated, self-correcting triage system that runs in the background.

Analyze Progress Reports via Agent Collaboration

The `sprint_reports` and `sprint_progress` tools expose burndown and member distribution charts to your agent conversation. A QA agent can flag a bottleneck in the testing column, prompting a scrum master agent to reassign tasks. By checking `sprint_stats`, the agents determine if the bottleneck is a recurring trend. They collaborate to draft a retrospective summary without human intervention.

Setup guide

Set up GitScrum Sprints 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 GitScrum Sprints 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="GitScrum Sprints_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

    result = await agent.run("List recent GitScrum Sprints 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 GitScrum. 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 GitScrum Sprints MCP in AutoGen

Install autogen-ext[mcp] and import mcp_server_tools with your Vinkius HTTP endpoint. Configure this MCP server by passing the generated tools list directly into your AssistantAgent constructor.

Yes, agents use `create_user_story` and `list_epics` to build out project hierarchies. They discuss the requirements in their chat channel before executing the tool calls.

If `sprint_progress` shows a lag but `sprint_metrics` suggests high velocity, your agents will debate the discrepancy. They query `list_tasks` to find the exact bottleneck before reaching a consensus.

This MCP server works with both stdio and Streamable HTTP transports. AutoGen uses the McpToolAdapter to automatically map the schemas for all 15 sprint-tracking tools.

Every tool execution on this MCP server runs inside a secure, zero-trust V8 sandbox. Your credentials, sprint_reports, and task queries are never exposed to other agents or external networks.

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