Can my AI agent list all workspaces and drill into specific project details?

Absolutely. Use `list_workspaces` to browse all your organizational workspaces, then `list_projects` to see every project within a workspace. Your agent returns full metadata including member counts, settings, and creation dates — all without leaving your conversation.

I need to set up a new project with custom workflows and labels — can I do it all conversationally?

Yes. Tell your agent to create the project, then ask it to configure the workflow columns and create color-coded labels. The agent chains `create_project`, `list_workflows`, and `create_label` tools automatically to set up your board exactly as you describe it.

Does this integration let me modify or delete projects?

This core integration focuses on workspace exploration, project creation, and configuration management — including workflows and labels. For task-level operations like CRUD, subtasks, and checklists, use the dedicated GitScrum Tasks integration. All actions respect your GitScrum account permissions.

GitScrum MCP Server for CrewAI 16 tools — connect in under 2 minutes

Name: GitScrum
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 16 Tools Framework

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

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="GitScrum Specialist",
    goal="Help users interact with GitScrum effectively",
    backstory=(
        "You are an expert at leveraging GitScrum 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 "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 16 available tools "
        "and what they can do."
    ),
)

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

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* 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 MCP Server

What you can do

Browse workspaces — list all your organizational workspaces and retrieve details for each one
Manage projects — list, create, and inspect projects with full metadata including members and settings
Configure workflows — view and manage Kanban column definitions and workflow templates
Organize with labels — list, create, and update color-coded labels to categorize work
Access your profile — retrieve the authenticated user's profile across all workspaces

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

The GitScrum MCP Server exposes 16 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 to CrewAI via MCP

Follow these steps to integrate the GitScrum 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 16 tools from GitScrum

Why Use CrewAI with the GitScrum MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with GitScrum 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 + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries GitScrum 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, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain GitScrum 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 against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

GitScrum MCP Tools for CrewAI (16)

These 16 tools become available when you connect GitScrum to CrewAI via MCP:

create_project

Create a new project

create_workspace

Create a new workspace

find_project

Find a project by name

get_me

Get authenticated user profile

get_project

Get project details

get_task

Get task details by UUID

get_workspace

Get workspace details

list_labels

List labels in a project

list_project_members

List members in a project

list_projects

List projects in a workspace

list_tasks

Filter by status (todo, in-progress, done). Essential for understanding project scope and workload. List tasks in a project

list_workflows

List workflows (columns) in a project

list_workspaces

List all GitScrum workspaces

my_role

Get my role in the workspace

project_stats

Get project statistics

workspace_stats

Get workspace statistics

Example Prompts for GitScrum in CrewAI

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

"Show me all the workspaces I have access to on GitScrum."

"Create a new project called 'Mobile App v2' in the acme-eng workspace with a description."

"What labels are available in the web-app project?"

Troubleshooting GitScrum MCP Server with CrewAI

Common issues when connecting GitScrum 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 + CrewAI FAQ

Common questions about integrating GitScrum 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.