Multi Orchestrator MCP

Enterprise-level multi-agent orchestration MCP server for coordinating professional agents in architecture, quality, cloud, and prompt to achieve autonomous software development, testing, and deployment, with self-healing, authentication, and analysis capabilities.

Developer tools Artificial intelligence chatbots #Agent orchestration #Autonomous development #Enterprise-level #Secure deployment .Python

rating : 2 points

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update time : 2025-12-12

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What is Multi Agent Orchestrator MCP?

This is an enterprise-level server based on the Model Context Protocol (MCP) that can coordinate multiple professional AI agents to work together to complete the entire software development process from requirements analysis to deployment. It's like a team of AI project managers, automatically assigning tasks to roles such as architects, test engineers, and cloud deployment experts to ensure high-quality project delivery.

How to use Multi Agent Orchestrator MCP?

You can connect to the server through clients that support the MCP protocol (such as VS Code, Cursor, Claude Desktop, etc.), and then use natural language to describe your development requirements. The server will automatically coordinate professional agents to complete tasks for you, including generating architectural designs, writing code, quality checks, cloud deployment, etc.

Applicable scenarios

Suitable for software development teams, independent developers, and technical managers in scenarios such as rapid prototyping, automated testing, cloud deployment optimization, and code quality assurance. Especially suitable for complex projects that require coordination of multiple technology stacks.

Main features

Multi-agent coordination

Coordinate four professional agents in architecture, quality, cloud, and prompt to work together, just like a real development team with division of labor and cooperation.

Self-healing ability

Automatically detect code errors and provide repair solutions, reducing manual debugging time.

Enterprise-level authentication

Integrate Descope to provide secure user authentication and permission management.

Intelligent analysis and monitoring

Provide usage analysis and performance monitoring through Cequence.

Automated cloud deployment

Automatically plan deployment strategies, verify environment configurations, and set up monitoring and rollback mechanisms.

Prompt optimization

Automatically optimize AI prompts based on goals and performance indicators to improve output quality.

Advantages

One-stop solution: A complete automated process from architectural design to deployment.

Professional division of labor: Different agents focus on different areas, providing professional-level output.

Enterprise-level security: Integrate professional authentication services to ensure data security.

Easy to integrate: Support all mainstream MCP clients without complex configuration.

Self-optimization: Have the ability to learn and improve, becoming more intelligent with use.

Limitations

Requires network connection: Depends on cloud services for authentication and analysis.

Learning curve: Need to understand the functional characteristics of each agent when using it for the first time.

Resource consumption: Multi-agent coordination requires more computing resources.

Specific scenarios: Most suitable for software development-related tasks and may not be applicable in other fields.

How to use

Select a client

Install a client that supports the MCP protocol, such as VS Code, Cursor, Claude Desktop, etc.

Configure the server

Add the Multi Agent Orchestrator MCP server to the client configuration.

Start using

Use natural language to describe your development requirements in the client, and the server will automatically coordinate agents to complete the tasks.

View results

The server will return complete development documents such as architectural designs, code implementations, and test plans.

Usage examples

Full-stack application development

Develop a complete full-stack application, including front-end interfaces, back-end APIs, and database design.

Code quality review

Conduct a quality check on existing code and provide improvement suggestions.

Cloud deployment planning

Plan a cloud deployment strategy for an application.

Frequently Asked Questions

Do I need programming experience to use this server?

Which programming languages and technology stacks does the server support?

How is data security ensured?

What is the server's response speed?

Can I customize agents or add new functions?

Related resources

Official deployment link

Deployment instance on the Smithery platform

GitHub repository

Source code and detailed technical documentation

MCP protocol official website

Official documentation of the Model Context Protocol

Quick start guide

Guide for MCP client configuration and usage

VS Code configuration guide

Detailed steps for configuring the MCP server in VS Code

🚀 Multi Agent Orchestrator MCP

An enterprise‑grade Model Context Protocol (MCP) server for autonomous software engineering. It coordinates specialized agents to handle application planning, building, testing, and deployment, featuring self‑healing, authentication, and analytics capabilities.

Check out the deployed link on the Smithery Platform: Smithery Platfrom Deployed Link

👥 Team

Team Name: UpsideDown
Member: Shreesaanth R

🏆 Hackathon

Theme 2: Build a Secure MCP Server for Agents (w/ Cequence)
Challenge addressed: Build a production‑ready MCP server that orchestrates multiple agents with authentication (Descope), hosting (Cequence), and self‑healing to reliably execute end‑to‑end development workflows, deployable on Smithery.

📋 Requirements

Python 3.11+
Git
An MCP‑compatible client (VS Code, Cursor, Windsurf, Claude Desktop, etc.)

🚀 Quick Start

First, install the server with your MCP client. For an overview of client support and mechanics, see the official MCP quickstart: MCP Quickstart

Standard config (works in most clients)

{
  "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"
      ]
    }
  }
}

💻 Client-specific Installation Guides

VS Code

One Click installation
Follow the MCP install guide: VS Code MCP Install Guide
Or via CLI:

# VS Code (stable)
npx -y @smithery/cli@latest install @yoriichi-07/multi_orchestrator_mcp --client vscode --key 70fd8cf1-9dd3-4556-8a43-78916f617fb2

# VS Code Insiders
npx -y @smithery/cli@latest install @yoriichi-07/multi_orchestrator_mcp --client vscode-insiders --key 70fd8cf1-9dd3-4556-8a43-78916f617fb2

Cursor

Click the button to install (if the deeplink is not supported on your OS, use the manual steps below):

Manual: Go to Cursor Settings → MCP → Add new MCP Server. Choose command type and set:

npx -y @smithery/cli@latest install @yoriichi-07/multi_orchestrator_mcp --client cursor --key 70fd8cf1-9dd3-4556-8a43-78916f617fb2

Claude Code

Use the CLI then paste the standard config above:

claude mcp add --transport http yoriichi-07-multi-orchestrator-mcp "https://server.smithery.ai/@yoriichi-07/multi_orchestrator_mcp/mcp"

Claude Desktop

Follow the MCP quickstart: Claude Desktop MCP Quickstart. Use the standard config above.

LM Studio

Use the Install → Edit mcp.json, paste the standard config. One-click:

Windsurf

Docs: Windsurf MCP Docs. Use the standard config above (replace URL with http://localhost:8080 for local).

🏠 Local Installation and Running

Run locally

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

Configure (optional)

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

MCP discovery path is served by FastMCP under the mounted app

</details>

<details>
<summary>Docker</summary>
```bash
docker build -t multi-orchestrator-mcp .
docker run --rm -p 8080:8080 --env-file .env multi-orchestrator-mcp

⚙️ Configuration

Environment variables (see config/env.template):

DESCOPE_PROJECT_ID, DESCOPE_MANAGEMENT_KEY, DESCOPE_ACCESS_KEY – enable Descope authentication (optional for local/dev).
PORT – server port (default 8080).
DESCOPE_DEMO_MODE – set true for local testing without full auth.
CEQUENCE_GATEWAY_ID, CEQUENCE_API_KEY – enable Cequence analytics (optional).
JWT_SECRET_KEY, CORS_ORIGINS, RATE_LIMIT_REQUESTS, logging toggles.

Client configuration templates are provided in config/mcp.json.template (direct JWT or auto‑refresh proxy modes).

🛠️ Capabilities

🛠️ Tools (12)

Tools Details

orchestrate_task
- Title: Orchestrate multi‑agent task
- Description: Coordinate Frontend/Backend/DevOps/QA agents for development, testing, or deployment.
- Parameters: task_description (string), task_type (enum: development|architecture|testing|deployment), priority (enum)
generate_architecture
- Title: Generate architecture
- Description: Produce system architecture with components and recommendations.
- Parameters: project_description (string), tech_stack (string[]), requirements (string[])
auto_fix_code
- Title: Self‑healing fix
- Description: Generate fixes for code using error context and explanations.
- Parameters: code (string), error_message (string), context (string)
list_capabilities
- Title: Catalog
- Description: Summarize available agents, tools, enterprise features, and supported tasks.
- Parameters: none
get_system_status
- Title: System status
- Description: Returns server health, agent availability, analytics/auth status, and timestamp.
- Parameters: none
advanced_generate_application
- Title: Enterprise app generation
- Description: Plan and generate an application using advanced agents and deployment strategies.
- Parameters: description (string), complexity_level (enum), innovation_requirements (string[]), deployment_strategy (enum)
autonomous_architect
- Title: Autonomous architect
- Description: Builds an execution DAG and adaptive strategy from goals and constraints.
- Parameters: project_goals (string[]), constraints (string[]), learning_objectives (string[])
proactive_quality_assurance
- Title: Proactive quality
- Description: Applies policy‑as‑code checks with optional auto‑remediation.
- Parameters: code_context (string), quality_standards (string[]), auto_remediation (bool)
evolutionary_prompt_optimization
- Title: Prompt evolution
- Description: Creates and evolves prompts based on goals and performance metrics.
- Parameters: base_prompt (string), optimization_goals (string[]), performance_metrics (object)
last_mile_cloud_deployment
- Title: Cloud deployment
- Description: Plans deployment, verifies environments, and returns rollback/monitoring setup.
- Parameters: application_context (string), target_environments (string[]), verification_requirements (string[])
ping
- Title: Health check
- Description: Simple liveness probe.
- Parameters: none
debug_server_config
- Title: Debug configuration (temporary)
- Description: Exposes non‑secret configuration metadata for diagnostics.
- Parameters: none