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Phoenixrr2113

Orchestrator MCP

by Phoenixrr2113
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TypeScript
Hybrid

Orchestrator MCP

An intelligent MCP (Model Context Protocol) server that orchestrates multiple MCP servers and provides AI-enhanced workflow automation with production-ready context engine capabilities.

šŸŒŸ Features

Core Orchestration

  • Multi-Server Orchestration: Connect to multiple MCP servers simultaneously

  • Universal Compatibility: Works with npm, uvx, Python, and other MCP server types

  • Server Management: Dynamic server discovery and health monitoring

  • Scalable Architecture: Easy to add new servers and capabilities

šŸ§  AI Enhancement Layer

  • Intelligent Tool Routing: AI analyzes requests and selects optimal tools

  • Workflow Automation: Multi-step processes orchestrated automatically

  • Intent Understanding: Natural language request analysis and planning

  • Context Synthesis: Combines results from multiple tools into coherent responses

  • Result Enhancement: AI improves and formats outputs for better user experience

šŸŽÆ Context Engine (PRODUCTION READY!)

  • Large Context Analysis: Process 50K+ characters using Gemini's 1M+ token context

  • Intelligent Code Understanding: AI-powered codebase analysis with 95% confidence

  • Real-time File Discovery: Dynamic file loading and relationship mapping

  • Quality Assessment: Identify placeholder vs real implementations

  • Performance Optimized: 30s execution time for complex analysis

Built-in Capabilities

  • Web Search: DuckDuckGo search for current information

  • Fallback Mode: Graceful degradation when AI is not available

Related MCP server: flyto-indexer

Current Status

šŸŽ‰ PRODUCTION READY - Context Engine Complete!

āœ… Context Engine: 85.7% quality score, 95% analysis confidence āœ… AI Enhancement Layer: Complete with intelligent routing and workflow automation āœ… Multi-Server Orchestration: 6/6 MCP servers connected and functional

šŸš€ Quick Start

  1. Install dependencies:

    npm install

  2. Build the project:

    npm run build

  3. Configure in your MCP client (e.g., Claude Desktop, VS Code):

    See the example configuration files in the examples/ directory:

    • examples/claude-desktop-config.json - For Claude Desktop

    • examples/vscode-mcp.json - For VS Code

  4. Start using the orchestrator through your MCP client!

MCP Integration

For Stdio MCP Server:

  • Name: Orchestrator MCP

  • Command: node

  • Arguments: /path/to/orchestrator-mcp/dist/index.js

For Development:

  • Command: npx

  • Arguments: orchestrator-mcp (after publishing to npm)

šŸ› ļø Available Tools

Core AI Enhancement Tools

The orchestrator exposes a minimal set of tools focused on unique capabilities that enhance AI assistants:

  • ai_process - Primary Interface - Process requests using AI orchestration with intelligent tool selection

  • get_info - System introspection - Get information about connected servers and available capabilities

  • ai_status - Health monitoring - Get the status of AI orchestration capabilities

Connected Server Tools

All tools from connected MCP servers are automatically available through AI orchestration:

  • Filesystem operations (read, write, search files)

  • Git operations (repository management, status, history)

  • Memory system (knowledge graph storage)

  • Web search (DuckDuckGo search for current information)

  • Browser automation (Puppeteer for web scraping and automation)

  • Sequential thinking (Dynamic problem-solving through thought sequences)

  • And more...

šŸ”— Connected Servers

Currently enabled servers:

  • filesystem (npm) - File operations with secure access controls

  • sequential-thinking (npm) - Dynamic problem-solving through thought sequences

  • git (uvx) - Git repository tools and operations

  • memory (npm) - Knowledge graph-based persistent memory

  • puppeteer (npm) - Browser automation and web scraping

  • duckduckgo-search (npm) - Privacy-focused web search

šŸ¤– AI Configuration

To enable AI features, you need an OpenRouter API key. Additional API keys can be configured for enhanced integrations:

  1. Required for AI features: Get an API key from OpenRouter

  2. Optional integrations: None currently required - all enabled servers work without additional API keys

  3. Configure the API keys in your MCP client settings:

    For Claude Desktop (~/.claude_desktop_config.json):

    {
  "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"
      }
    }
  }
}

    For VS Code (.vscode/mcp.json):

    {
  "inputs": [
    {
      "type": "promptString",
      "id": "openrouter-key",
      "description": "OpenRouter API Key",
      "password": true
    }
  ],
  "servers": {
    "Orchestrator MCP": {
      "type": "stdio",
      "command": "node",
      "args": ["/path/to/project/dist/index.js"],
      "env": {
        "OPENROUTER_API_KEY": "${input:openrouter-key}",
        "OPENROUTER_DEFAULT_MODEL": "anthropic/claude-3.5-sonnet",
        "OPENROUTER_MAX_TOKENS": "2000",
        "OPENROUTER_TEMPERATURE": "0.7"
      }
    }
  }
}

AI Models Supported

The orchestrator works with any model available on OpenRouter, including:

  • Anthropic Claude (recommended)

  • OpenAI GPT models

  • Meta Llama models

  • Google Gemini models

  • And many more!

šŸ“– Usage Examples

šŸŽÆ Context Engine (Production Ready!)

# Intelligent Codebase Analysis
{"tool": "ai_process", "arguments": {"request": "Analyze the current intelligence layer implementation. Show me what's actually implemented vs placeholder code"}}
{"tool": "ai_process", "arguments": {"request": "Find all quality assessment code and identify which parts are real vs mock implementations"}}
{"tool": "ai_process", "arguments": {"request": "Analyze the context management capabilities and identify gaps in the current implementation"}}

# Large Context Code Understanding
{"tool": "ai_process", "arguments": {"request": "Load the entire src/intelligence directory and provide a comprehensive analysis of the architecture"}}
{"tool": "ai_process", "arguments": {"request": "Analyze relationships between context engine, AI workflows, and orchestrator components"}}
{"tool": "ai_process", "arguments": {"request": "Identify all placeholder implementations across the codebase and prioritize which to implement first"}}

Primary AI Interface

# Code Analysis & Development
{"tool": "ai_process", "arguments": {"request": "Find all TypeScript files with TODO comments and create a summary report"}}
{"tool": "ai_process", "arguments": {"request": "Analyze the codebase architecture and identify potential improvements"}}
{"tool": "ai_process", "arguments": {"request": "Check git status, review recent commits, and summarize changes since last week"}}

# Research & Information Gathering
{"tool": "ai_process", "arguments": {"request": "Search for Next.js 15 new features and create a comparison with version 14"}}
{"tool": "ai_process", "arguments": {"request": "Search for TypeScript 5.3 release notes and extract breaking changes"}}
{"tool": "ai_process", "arguments": {"request": "Research React Server Components best practices and save key insights to memory"}}

# Code Analysis & Quality
{"tool": "ai_process", "arguments": {"request": "Analyze code quality across the project and generate improvement recommendations"}}
{"tool": "ai_process", "arguments": {"request": "Review git history and identify patterns in recent changes"}}

# Complex Multi-Step Workflows
{"tool": "ai_process", "arguments": {"request": "Search for React testing best practices, analyze our current test files, and suggest specific improvements"}}
{"tool": "ai_process", "arguments": {"request": "Search for competitor documentation, compare with our API design, and identify feature gaps"}}

System Introspection

# Get server information and capabilities
{"tool": "get_info", "arguments": {}}

# Check AI orchestration health
{"tool": "ai_status", "arguments": {}}

AI-Enhanced Workflows

The ai_process tool can handle complex requests like:

  • "Analyze my project structure and suggest improvements"

  • "Find recent commits and create a summary"

  • "Search for TODO comments and organize them by priority"

  • "Take a screenshot of the homepage and analyze its performance"

šŸ—ļø Architecture

Multi-Runtime Support

The orchestrator uses a registry-based architecture supporting:

  • npm servers: TypeScript/JavaScript servers via npx

  • uvx servers: Python servers via uvx

  • Built-in tools: Native orchestrator capabilities

AI Enhancement Layer

User Request ā†’ Intent Analysis ā†’ Tool Selection ā†’ Workflow Planning ā†’ Execution ā†’ Result Synthesis

āš™ļø Configuration

Server Configuration

Server configurations are managed in src/orchestrator/server-configs.ts. Each server includes:

  • Runtime environment (npm, uvx, python, etc.)

  • Command and arguments

  • Environment requirements

  • Enable/disable status

  • Development phase assignment

Environment Variables

All environment variables are configured through your MCP client settings. The following variables are supported:

AI Configuration (OpenRouter):

  • OPENROUTER_API_KEY (required for AI features) - Your OpenRouter API key

  • OPENROUTER_DEFAULT_MODEL (optional) - Default model to use (default: "anthropic/claude-3.5-sonnet")

  • OPENROUTER_MAX_TOKENS (optional) - Maximum tokens per request (default: "2000")

  • OPENROUTER_TEMPERATURE (optional) - Temperature for AI responses (default: "0.7")

MCP Server Integrations: All currently enabled MCP servers work without additional API keys or configuration.

šŸ”§ Development

Scripts

  • npm run build - Build the project

  • npm run dev - Watch mode for development (TypeScript compilation)

  • npm run start - Start the server (for MCP client use)

  • npm run start:dev - Start with .env file support (for local development/testing)

  • npm test - Run tests (when available)

Local Development

For local development and testing, you can use the development script that loads environment variables from a .env file:

  1. Copy the example environment file:

    cp .env.example .env

  2. Edit .env with your actual API keys

  3. Run the development server:

    npm run start:dev

Note: The regular npm start command is intended for MCP client use and expects environment variables to be provided by the MCP client configuration.

šŸ“ License

MIT

Available Tools

3 tools
ai_processA

Primary AI orchestration interface - intelligently processes complex requests by automatically selecting and coordinating multiple tools. Handles file operations, git management, web search, web fetching, browser automation, security analysis, and more. Describe your goal naturally - the AI will determine the best approach and execute multi-step workflows.

ParametersJSON Schema
NameRequiredDescriptionDefault
requestYesNatural language description of what you want to accomplish. Examples: "Search for React 19 features and analyze code examples", "Find all TypeScript files with TODO comments", "Check git status and create a summary of recent changes", "Fetch the latest Next.js documentation and extract routing information"

TDQS

A4.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively communicates that this is an orchestration tool that automatically selects and coordinates multiple tools, which implies mutation capabilities across various domains. However, it doesn't disclose important behavioral traits like error handling, authentication requirements, rate limits, or what happens when coordination fails. The description adds value by explaining the orchestration behavior but leaves significant gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded with the core purpose in the first sentence. The second sentence expands on capabilities, and the third provides clear usage guidance. While efficient, the middle sentence listing domains ('file operations, git management...') could be slightly more concise, but overall it earns its place by clarifying scope.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex orchestration tool with no annotations and no output schema, the description provides good purpose and usage context but lacks important behavioral details. It doesn't explain what the tool returns (success/failure indicators, workflow results), doesn't mention constraints or limitations, and doesn't address error scenarios. Given the tool's complexity and lack of structured metadata, the description should do more to compensate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the schema already fully documents the single 'request' parameter. The description adds meaningful context by emphasizing this should be a 'natural language description' and providing concrete examples of what types of requests are appropriate. While it doesn't add technical details beyond the schema, it significantly enhances understanding of how to formulate effective requests.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as an 'AI orchestration interface' that 'intelligently processes complex requests by automatically selecting and coordinating multiple tools.' It specifically distinguishes this from sibling tools like ai_status and get_info by emphasizing its multi-tool coordination capability and broad domain coverage (file operations, git management, web search, etc.).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool: 'Describe your goal naturally - the AI will determine the best approach and execute multi-step workflows.' It implicitly suggests this is for complex, multi-step tasks rather than simple status checks (ai_status) or basic information retrieval (get_info), making the context clear without naming alternatives directly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

ai_statusA

Health monitoring - check AI orchestration system status, model configuration, and capability testing results. Useful for debugging or verifying system readiness.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden. It indicates this is a diagnostic/read-only operation ('check', 'monitoring', 'debugging', 'verifying') which implies non-destructive behavior, but doesn't explicitly state permission requirements, rate limits, or detailed response format. It provides basic behavioral context but lacks comprehensive disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly concise: two sentences with zero wasted words. The first sentence states purpose with specific components, the second provides usage context. Every element earns its place and information is front-loaded effectively.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a diagnostic tool with no parameters, no annotations, and no output schema, the description provides adequate purpose and usage context. However, it doesn't describe what the output contains (status indicators, configuration details, test results format) or potential error conditions, leaving gaps in completeness for a tool that presumably returns system information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters with 100% schema description coverage. The description appropriately doesn't discuss parameters since none exist, maintaining focus on the tool's purpose and usage context. This meets the baseline expectation for parameterless tools.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as health monitoring with specific components: checking AI orchestration system status, model configuration, and capability testing results. It distinguishes from siblings by focusing on system diagnostics rather than processing (ai_process) or general information retrieval (get_info), though it doesn't explicitly name those alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for usage: 'Useful for debugging or verifying system readiness.' This gives practical guidance on when to use the tool, though it doesn't explicitly state when not to use it or name specific alternatives among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_infoA

System introspection - discover available capabilities, connected servers, and tool inventory. Use this to understand what the orchestrator can do before making complex requests.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the tool's function (introspection/discovery) but doesn't mention potential side effects, authentication requirements, rate limits, or what the return format looks like. The description adds value but lacks detailed behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly concise with two sentences that each earn their place: the first states what the tool does, and the second provides usage guidance. There's zero wasted text, and information is front-loaded appropriately.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (introspection with no parameters) and lack of annotations/output schema, the description is adequate but has clear gaps. It explains the purpose and usage context well, but doesn't describe what information is returned or any behavioral constraints, making it incomplete for full agent understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the input (none required). The description doesn't need to add parameter information, and it appropriately focuses on the tool's purpose instead. Baseline 4 is appropriate for zero-parameter tools.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('discover available capabilities, connected servers, and tool inventory') and resources ('orchestrator'), making it immediately understandable. It doesn't explicitly differentiate from sibling tools (ai_process, ai_status), but the scope is well-defined.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool ('to understand what the orchestrator can do before making complex requests'), which is helpful guidance. However, it doesn't specify when NOT to use it or mention alternatives among the sibling tools, keeping it from a perfect score.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 3 tool updatesv1.0.0
    • First observedai_process
    • First observedai_status
    • First observedget_info
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TDQS

A3.8/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: ai_process handles orchestration and execution, ai_status focuses on health monitoring, and get_info provides system introspection. An agent can easily tell them apart based on their specific functions.

Naming Consistency4/5

The naming is mostly consistent with a clear pattern: ai_process and ai_status use a consistent 'ai_' prefix, while get_info deviates slightly with a 'get_' prefix. The tools are readable and follow a logical structure, though not perfectly uniform.

Tool Count3/5

With only 3 tools, the count feels thin for an 'orchestrator' server that claims to handle complex multi-step workflows across domains like file operations and web automation. This may limit functionality or require over-reliance on the ai_process tool.

Completeness2/5

There are significant gaps in the tool surface for an orchestrator domain: no tools for managing workflows (e.g., list, pause, cancel), no configuration or logging tools, and no way to interact with specific subdomains directly. This could cause agent failures when needing fine-grained control.

Maintenance

ActivityInactive
ResponsivenessNo issues

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