How to Use the GitScrum Sprints MCP in OpenAI Agents SDK

Build production-grade sprint agents with OpenAI Agents SDK. Manage cycles and metrics with full execution tracing.

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Connect GitScrum Sprints MCP to OpenAI Agents SDK

Create your Vinkius account to connect GitScrum Sprints to OpenAI Agents SDK 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 OpenAI Agents SDK

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Automate sprint management in OpenAI Agents SDK

Stop manually tracking tasks. Your agent uses `create_sprint` and `update_sprint` to keep your project cycles moving without you jumping between tabs. It handles the heavy lifting by pulling data through `all_sprints` and `list_tasks`. Your agent stays updated on the latest shift in priorities.

Real-time KPI tracking for OpenAI Agents SDK

Feed your agent current data using `sprint_kpis` and `sprint_progress`. It catches bottlenecks before they turn into missed deadlines. Your agent interprets `sprint_reports` to explain exactly where the team stands. You get clear answers instead of digging through raw data.

Structured sprint data for OpenAI Agents SDK

Organize your workflow using `create_user_story` and `list_epics`. Your agent structures the backlog so you can focus on shipping features. It retrieves `get_task` details instantly. This MCP Server ensures your agent works with the exact state of your sprint.

Setup guide

Set up GitScrum Sprints MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all GitScrum Sprints tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives GitScrum Sprints tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate GitScrum Sprints tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="GitScrum Sprints Agent",
            instructions="You have access to GitScrum Sprints tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about GitScrum Sprints MCP in OpenAI Agents SDK

Use the MCPServerStreamableHttp class in your agent constructor. Pass the server URL directly to register all tools for immediate use.

Yes, keep your agent responsive by setting cacheToolsList to True. This minimizes overhead when querying across large sprint histories.

Absolutely. Because tools are auto-discovered, your primary agent can delegate sprint-specific tasks to specialized agents without configuration changes.

The built-in guardrails in your agent will catch the error. You can view the full trace in your dashboard to debug the specific action.

Your sprint data remains scoped to your session. The server acts as a secure proxy, ensuring only authorized tasks and metrics are accessed.

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