Can my AI agent show me the burndown chart data for the current sprint?

Yes! Use `sprint_reports` with the resource set to 'burndown'. Your agent returns the ideal versus actual burn-down data points, so you can visualize or analyze sprint health instantly. You can also request 'burnup', 'performance', or 'member_distribution' reports.

Can I see what tasks are in a specific sprint?

Absolutely. Use `list_tasks` with the `sprint_slug` filter to see only tasks belonging to that sprint. You can further filter by status (todo, in-progress, done) to focus on what matters. Then use `get_task` to drill into any specific task for full details.

Does this integration support sprint velocity and metrics tracking?

Yes. Use `sprint_metrics` for detailed velocity, throughput, and efficiency data. Combined with `sprint_kpis` for high-level indicators and `sprint_stats` for task distribution analysis, you get a complete performance picture across any sprint.

GitScrum Sprints MCP Server for LangChain 15 tools — connect in under 2 minutes

Name: GitScrum Sprints
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 15 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect GitScrum Sprints through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "gitscrum-sprints": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using GitScrum Sprints, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

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IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About GitScrum Sprints MCP Server

What you can do

Sprint lifecycle — create, update, delete, and inspect sprints with precise date ranges and configurations
Performance analytics — access sprint KPIs, detailed statistics, progress tracking, and velocity metrics in real-time
Visual reports — retrieve burndown, burnup, performance, and distribution chart data for any sprint
Backlog management — list and create user stories, browse epics, and view tasks filtered by sprint
Cross-workspace visibility — list sprints across all workspaces for portfolio-level oversight

LangChain's ecosystem of 500+ components combines seamlessly with GitScrum Sprints through native MCP adapters. Connect 15 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

The GitScrum Sprints MCP Server exposes 15 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect GitScrum Sprints to LangChain via MCP

Follow these steps to integrate the GitScrum Sprints MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 15 tools from GitScrum Sprints via MCP

Why Use LangChain with the GitScrum Sprints MCP Server

LangChain provides unique advantages when paired with GitScrum Sprints through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine GitScrum Sprints MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across GitScrum Sprints queries for multi-turn workflows

GitScrum Sprints + LangChain Use Cases

Practical scenarios where LangChain combined with the GitScrum Sprints MCP Server delivers measurable value.

RAG with live data: combine GitScrum Sprints tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query GitScrum Sprints, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain GitScrum Sprints tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every GitScrum Sprints tool call, measure latency, and optimize your agent's performance

GitScrum Sprints MCP Tools for LangChain (15)

These 15 tools become available when you connect GitScrum Sprints to LangChain via MCP:

all_sprints

List sprints across all workspaces

create_sprint

Create a new sprint

create_user_story

Create a user story

get_sprint

Get sprint details

get_task

Get task details by UUID

list_epics

List epics in a project

list_sprints

List sprints in a project

list_tasks

Use the sprint_slug filter to see only tasks belonging to a specific sprint. Filter by status (todo, in-progress, done). List tasks in a project, optionally filtered by sprint

list_user_stories

List user stories in a project

sprint_kpis

Get sprint KPIs

sprint_metrics

Get detailed sprint metrics

sprint_progress

Get current sprint progress

sprint_reports

Resource: burndown, burnup, performance, types, efforts, member_distribution, task, type_distribution. Get sprint reports with charts

sprint_stats

Get sprint statistics

update_sprint

Update an existing sprint

Example Prompts for GitScrum Sprints in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with GitScrum Sprints immediately.

"What's the progress of our current sprint in the web-app project?"

"Create a new sprint 'Sprint 15 — Payments' from April 14 to April 28."

"Show me the velocity metrics for the last completed sprint."

Troubleshooting GitScrum Sprints MCP Server with LangChain

Common issues when connecting GitScrum Sprints to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

GitScrum Sprints + LangChain FAQ

Common questions about integrating GitScrum Sprints MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.