What is the maximum team size?
The maximum team size is configurable via the `MAX_CREW_SIZE` environment variable, with a default of 10.
@Hwani-Net
Basic information
Dynamic Team Collaboration MCP Server for Smithery - Flexible crew synchronization with priority-based task management
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"crew-sync-agent": {
"command": "python",
"args": [
"test_crew_sync.py"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
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.
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.
The maximum team size is configurable via the `MAX_CREW_SIZE` environment variable, with a default of 10.
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.
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).
Create a GitHub repository, then run `smithery deploy` with your repository URL. Configure required environment variables in the Smithery dashboard.
The system uses Python and environment variables for configuration. Optional AI services (OpenAI, Anthropic) require corresponding API keys. Docker is recommended for deployment.
emergency-medicare-planner-mcp-server
MCP server that allow LLM in agent mode builds n8n workflows for you
Agentic SWMM is an automated, auditable, and memory-informed framework for reproducible stormwater modelling, integrating QGIS and EPA SWMM through the aiswmm runtime, reusable Skills, and MCP interfaces, with QA verification, provenance tracking, calibration support, and Codex,
A comprehensive Model Context Protocol (MCP) server providing AI assistants with powerful task management and agent memories capabilities with project-specific storage.
Turn any JavaScript module into a sandboxed MCP (Model Context Protocol) server with automatic reflection and type inference.
Comments