Orchestrator MCP Server

🚀 Workflow Orchestrator MCP Server

The Workflow Orchestrator MCP Server implements a system for managing and executing complex workflows, leveraging AI-driven decision-making capabilities. It automatically coordinates and supervises distributed tasks and services by defining clear steps, conditions, and goals.

📜 Background

As workflows in modern applications become increasingly complex, manual management of these processes is both time-consuming and error-prone. This project aims to provide an intelligent solution that uses advanced AI technology to optimize workflow management, improve efficiency, and reduce the risk of human error.

📐 Architecture Overview

The system architecture mainly consists of the following core components:

1. MCP Server: Responsible for receiving and processing requests from external systems or users, and coordinating the execution of each step.
2. Workflow Definitions: A series of workflow templates are defined in YAML format. Each template details the dependencies between tasks, conditional judgments, and goal settings.
3. AI-driven Engine: Based on the Gemini model, it provides intelligent decision support and can generate optimal execution strategies according to the current state and preset rules.

💻 Workflow Example

Here is a typical Web application deployment workflow:

1. Environment Preparation
2. Code Distribution
3. Dependency Installation
4. Database Migration
5. Application Startup
6. Health Check

🧩 Components

MCP Server Functions

• Receive and parse workflow requests.
• Coordinate the execution order of various tasks.
• Handle conditional judgments and branching logic.
• Summarize and feedback execution results.
• Implement exception handling and recovery mechanisms.

Workflow Definition Format

It uses the YAML format and includes the following key fields:

• workflow_id: Unique identifier.
• steps: List of task steps.
• conditions: Conditional judgment rules.
• goals: Goal configuration.
• dependencies: Dependency relationships.

AI-driven Engine Functions

• Provide intelligent decision support.
• Automatically generate optimization strategies.
• Implement dynamic priority adjustment.
• Handle complex logical reasoning.

⚙️ Configuration

Configuration is done through environment variables. The specific parameters are as follows:

🚀 Quick Start / Running the Server

Install Dependencies

uv sync

Run the Server

uv run python -m orchestrator.server

Example Workflow Definition

workflow_id: deploy_web_app
steps:
  - id: prepare_env
    type: execute_command
    command: "source setup.sh"
  - id: distribute_code
    type: git_checkout
    branch: main
dependencies:
  - step_id: prepare_env
    must_succeed_before: true

🚧 Development Status

The project is currently in the alpha version. The main functions have been initially implemented. Future work will focus on improving the system's stability and scalability.

🧪 Testing Strategy

A layered testing strategy is adopted:

1. Unit Testing: Verify the independent functions of each component.
2. Integration Testing: Ensure that all components work together correctly.
3. Performance Testing: Evaluate the system's performance under high load.
4. Security Testing: Detect potential security vulnerabilities.
5. User Acceptance Testing (UAT): Collect user feedback for optimization.

📝 Workflow Instance

Here is a typical workflow execution log:

INFO: Starting execution of workflow deploy_web_app
INFO: Step prepare_env executed successfully
INFO: Step distribute_code is in progress...
INFO: Step distribute_code completed
INFO: The target state has been reached, and the workflow is completed

🖥️ Environment Requirements

• Operating System: Linux or macOS
• Python Version: 3.8+
• Required Dependencies: uv, orchestrator