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Multi Orchestrator MCP Server

Orchestrates architecture, quality, cloud, and prompt agents to plan, build, test, and deploy end‑to‑end workflows.

Installation

Add the following to your MCP client configuration file.

Configuration

{
  "mcpServers": {
    "yoriichi-07-multi_orchestrator_mcp": {
      "url": "http://localhost:8080/mcp",
      "headers": {
        "CEQUENCE_API_KEY": "api_key_123",
        "DESCOPE_ACCESS_KEY": "YYYYY-YYYYY",
        "DESCOPE_PROJECT_ID": "proj_abc123",
        "CEQUENCE_GATEWAY_ID": "gateway_1",
        "DESCOPE_MANAGEMENT_KEY": "XXXXX-XXXXX"
      }
    }
  }
}

You are using an enterprise‑grade Model Context Protocol (MCP) server that coordinates specialized agents to plan, build, test, and deploy applications with self‑healing, authentication, and analytics. This MCP server helps you orchestrate Architecture, Quality, Cloud, and Prompt agents to execute end‑to‑end development workflows with reliability and visibility.

How to use

Start by connecting an MCP client to the Multi Orchestrator MCP server. Use the standard MCP configuration to register the server with your client so you can begin planning tasks, generating architectures, and coordinating autonomous agents. Once connected, you can orchestrate tasks such as architectural planning, automated quality checks, and deployment steps, with self‑healing and analytics enabled when you configure authentication and analytics options.

How to install

Prerequisites: ensure you have Python 3.11+ and Git installed. You also need an MCP‑compatible client (for example, a VS Code MCP extension, Cursor, Windsurf, Claude Desktop, etc.). You should have network access to download dependencies and access to an MCP server endpoint if you plan to use HTTP transport.

{
  "mcpServers": {
    "multi_orchestrator_mcp": {
      "command": "cmd",
      "args": [
        "/c",
        "npx",
        "-y",
        "@smithery/cli@latest",
        "run",
        "@yoriichi-07/multi_orchestrator_mcp",
        "--key",
        "70fd8cf1-9dd3-4556-8a43-78916f617fb2"
      ]
    }
  }
}

Additional steps

You can run the MCP server locally using Python for HTTP transport. The default local address is http://localhost:8080, and the MCP discovery path is served by FastMCP under the mounted app. You can also build and run a Docker container if you prefer containerized deployment.

# Clone and install
git clone https://github.com/yoriichi-07/Multi_Orchestrator_MCP.git
cd Multi_Orchestrator_MCP
pip install -r requirements.txt

# Optional: configure environment
cp config/env.template .env
# edit .env with DESCOPE_* if you want auth and analytics

# Start the MCP server (HTTP transport)
python mcp_server.py

# Default: http://localhost:8080

Configuration and security notes

Environment variables can enable authentication and analytics. Typical variables to configure include DESCOPE_PROJECT_ID, DESCOPE_MANAGEMENT_KEY, DESCOPE_ACCESS_KEY for authentication, and CEQUENCE_GATEWAY_ID, CEQUENCE_API_KEY for analytics. You can also adjust JWT secrets, CORS origins, and rate limiting to fit your security requirements.

For production deployments, consider using Docker to containerize the server and provide a dedicated environment file for secrets. Ensure you set appropriate access controls and monitor analytics to detect unusual activity.

Troubleshooting and tips

If you encounter connectivity issues, verify that the MCP server is running and listening on the expected port (default 8080). Check that your MCP client is configured with the correct server name and key. Review logs for authentication errors or transport failures and adjust DESCOPE or Cequence configurations as needed.

Available tools

orchestrate_task

Coordinate Frontend/Backend/DevOps/QA agents for development, testing, or deployment.

generate_architecture

Produce system architecture with components and recommendations.

auto_fix_code

Generate fixes for code using error context and explanations.

list_capabilities

Catalog and summarize available agents, tools, and enterprise features.

get_system_status

Provide server health, agent availability, analytics/auth status, and timestamp.

advanced_generate_application

Plan and generate an application using advanced agents and deployment strategies.

autonomous_architect

Build an execution DAG and adaptive strategy from goals and constraints.

proactive_quality_assurance

Apply policy‑as‑code checks with optional auto‑remediation.

evolutionary_prompt_optimization

Create and evolve prompts based on goals and performance metrics.

last_mile_cloud_deployment

Plan deployment, verify environments, and return rollback/monitoring setup.

ping

Health check endpoint for liveness.

debug_server_config

Expose non‑secret configuration metadata for diagnostics.