How do I register an application in Crowd?

Log in to the Crowd console as an administrator, navigate to **Applications**, and click **Add Application**. Follow the wizard to define the name and password used for this integration.

What permissions are required?

The application registered in Crowd must have the 'Allow to authenticate users' permission and be mapped to the directories you want the agent to manage.

Can the agent reset user passwords?

Currently, the integration supports creating users with an initial password. Direct password resets for existing users should be handled via the Crowd console or self-service portal.

Atlassian Crowd MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Atlassian Crowd
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Atlassian Crowd through Vinkius, pass the Edge URL in the `mcps` parameter and every Atlassian Crowd 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="Atlassian Crowd Specialist",
    goal="Help users interact with Atlassian Crowd effectively",
    backstory=(
        "You are an expert at leveraging Atlassian Crowd 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 Atlassian Crowd "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

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

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About Atlassian Crowd MCP Server

Integrate Atlassian Crowd, the single sign-on and identity management application, directly into your AI workflow. Manage your corporate user directories, audit group memberships, and provision new accounts using natural language.

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

What you can do

User Management — List, search, and retrieve detailed profiles for active and inactive users.
Group Control — Monitor security groups and manage organizational memberships seamlessly.
Account Provisioning — Create new user accounts with full attribute definitions via chat.
Identity Auditing — Search for users by attributes and verify directory-wide security restrictions.

The Atlassian Crowd MCP Server exposes 10 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 Atlassian Crowd to CrewAI via MCP

Follow these steps to integrate the Atlassian Crowd 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 10 tools from Atlassian Crowd

Why Use CrewAI with the Atlassian Crowd MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Atlassian Crowd 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

Atlassian Crowd + CrewAI Use Cases

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

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

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

Atlassian Crowd MCP Tools for CrewAI (10)

These 10 tools become available when you connect Atlassian Crowd to CrewAI via MCP:

create_new_user

Provision a new user account in Crowd

get_group_details

Get details for a specific group

get_user_details

Get full profile and attributes for a specific user

list_active_users

List all active users managed in Crowd

list_all_groups

List all security and organizational groups

list_group_members

List all users who are members of a specific group

list_inactive_users

List all disabled or inactive user accounts

list_user_memberships

List all groups a specific user belongs to

search_users_by_attribute

Search for users using a CQL-like restriction string

search_users_by_name

Search for users whose name starts with a prefix

Example Prompts for Atlassian Crowd in CrewAI

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

"List all active users in the 'Internal Staff' directory."

"Which groups does user 'jsmith' belong to?"

"Search for users with an email address ending in '@vinkius.com'."

Troubleshooting Atlassian Crowd MCP Server with CrewAI

Common issues when connecting Atlassian Crowd 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.

Atlassian Crowd + CrewAI FAQ

Common questions about integrating Atlassian Crowd 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.