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 CrewAI 15 tools — connect in under 2 minutes

Name: GitScrum Sprints
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 15 Tools Framework

Connect your CrewAI agents to GitScrum Sprints through Vinkius, pass the Edge URL in the `mcps` parameter and every GitScrum Sprints tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="GitScrum Sprints Specialist",
    goal="Help users interact with GitScrum Sprints effectively",
    backstory=(
        "You are an expert at leveraging GitScrum Sprints tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in GitScrum Sprints "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 15 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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

When paired with CrewAI, GitScrum Sprints becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call GitScrum Sprints tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

The GitScrum Sprints MCP Server exposes 15 tools through the Vinkius. Connect it to CrewAI 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 CrewAI via MCP

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

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 15 tools from GitScrum Sprints

Why Use CrewAI with the GitScrum Sprints MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with GitScrum Sprints through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

GitScrum Sprints + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries GitScrum Sprints for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries GitScrum Sprints, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain GitScrum Sprints tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries GitScrum Sprints against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

GitScrum Sprints MCP Tools for CrewAI (15)

These 15 tools become available when you connect GitScrum Sprints to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

GitScrum Sprints + CrewAI FAQ

Common questions about integrating GitScrum Sprints MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.