🔄 Crew Sync Agent

What is Crew Sync Agent?

Crew Sync Agent is a flexible, dynamic team collaboration system built for the Model Context Protocol (MCP). It allows for dynamic team composition and real-time synchronization of crew members for various projects, unlike traditional fixed-team approaches.

How to use Crew Sync Agent?

Test locally with python test_crew_sync.py. For deployment, create a GitHub repository and deploy to Smithery using smithery deploy https://github.com/username/crew-sync-agent, then configure environment variables in the Smithery dashboard. Use the provided tools (sync_crew, add_crew_member, list_crew, echo) to manage and synchronize team members.

Key features of Crew Sync Agent

• Dynamic Team Sync for flexible project-specific composition
• Scalable Teams with support for up to 10 members
• Priority-Based task prioritization (Low/Medium/High/Urgent)
• Secure API key management via environment variables
• Real-time MCP protocol-based instant team synchronization

Use cases of Crew Sync Agent

• Web development projects (e.g., React + Node.js e-commerce platform)
• Data analysis projects (e.g., user behavior pattern analysis)
• Any project requiring dynamic team composition and task assignment

FAQ from Crew Sync Agent

What is the maximum team size?

The maximum team size is configurable via the MAX_CREW_SIZE environment variable, with a default of 10.

How are API keys managed?

API keys are managed exclusively through environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY). No hardcoded secrets exist in the code, and Docker runs as a non-root user for additional security.

What tools does Crew Sync Agent provide?

It provides sync_crew (synchronize team members for a task), add_crew_member (add a new member), list_crew (list current team), and echo (connection test).

How do I deploy Crew Sync Agent?

Create a GitHub repository, then run smithery deploy with your repository URL. Configure required environment variables in the Smithery dashboard.

What runtime dependencies are needed?

The system uses Python and environment variables for configuration. Optional AI services (OpenAI, Anthropic) require corresponding API keys. Docker is recommended for deployment.