Orchestrator MCP

Orchestrator MCP is a production-ready hub that coordinates multiple MCP servers and enhances workflows with intelligent AI-assisted context management. It unifies diverse server types, enables real-time orchestration, and delivers a streamlined path from intent to action across your tooling ecosystem.

How to use

You interact with Orchestrator MCP through your MCP client to access AI-enhanced orchestration across connected servers. Start your client, connect to the Orchestrator MCP, and use the built-in AI tools to route requests, plan multi-step workflows, and synthesize results from multiple servers into coherent responses.

How to install

Prerequisites: ensure you have Node.js and npm installed on your system.

# Install dependencies for the project
npm install

# Build the project
npm run build

Configuration

Configure AI features and MCP client connections to enable seamless orchestration across all connected MCP servers. The Orchestrator MCP exposes minimal AI tools and supports a range of connected servers for file, code, Git, memory, web search, and browser automation tasks.

For Claude Desktop and VS Code clients, you provide an MCP configuration that includes the Orchestrator MCP as a server. The following example shows how to wire the Orchestrator MCP into a client with OpenRouter keys and model settings.

AI Configuration

To enable AI features, obtain an OpenRouter API key. You can configure additional keys for enhanced integrations if needed. The primary required key is the OpenRouter API key.

Example client configuration for Claude Desktop and VS Code is shown below. Replace placeholders with your actual values.

{
  "mcpServers": {
    "Orchestrator MCP": {
      "command": "node",
      "args": ["/path/to/project/dist/index.js"],
      "env": {
        "OPENROUTER_API_KEY": "your_api_key_here",
        "OPENROUTER_DEFAULT_MODEL": "anthropic/claude-3.5-sonnet",
        "OPENROUTER_MAX_TOKENS": "2000",
        "OPENROUTER_TEMPERATURE": "0.7"
      }
    }
  }
}

Connected servers

Orchestrator MCP connects to multiple servers that provide core capabilities like filesystem access, version control, memory storage, browser automation, and web search. These servers are exposed through AI orchestration to enable seamless, cross-tool workflows.

Usage examples

Use the AI-enabled interfaces to query the system, request multi-step workflows, and retrieve synthesized results. The AI layer routes tasks to the appropriate tools, plans the workflow, executes steps across connected servers, and presents a coherent final result.

MCP integration details

For production integration, you can run Orchestrator MCP as a standard stdio MCP server or use development commands to run it locally. The official path for a runtime deployment uses a node process that launches the Orchestrator MCP entry point.

In development mode, you can run the orchestrator via a local command after publishing to npm.

Architecture overview

The orchestrator supports multi-runtime operation with npm, uvx, and built-in tools. A typical flow is: User Request → Intent Analysis → Tool Selection → Workflow Planning → Execution → Result Synthesis. This enables processing across multiple servers and tools with production-grade context management.

Development and scripts

Common development scripts include building, watching for changes, starting the server, and running tests when available.

Local development can load environment variables from a .env file. Copy the example environment file and customize it for your setup, then run the development server.

Available tools

ai_process

Primary interface that processes requests using AI orchestration with intelligent tool selection

get_info

System introspection - retrieve information about connected servers and capabilities

ai_status

Health monitoring - report the status of AI orchestration features

filesystem

Filesystem operations such as read, write, and search with secure access controls

git

Git operations including repository management, status, and history

memory

Knowledge graph storage and retrieval in memory systems

puppeteer

Browser automation for web scraping and automation tasks

sequential-thinking

Dynamic problem-solving using structured thought sequences