Maestro 🎭

Intelligent Workflow Orchestration for Agentic IDEs

Maestro is an MCP (Model Context Protocol) server that provides 11 specialized orchestration tools to dramatically enhance LLM capabilities in IDEs like Cursor and Claude Desktop. Smithery and other MCP tool catalogs will discover these tools and make them available for intelligent workflow orchestration with modular intelligence amplification.

🛠️ Complete Tool Catalog (11 Tools)

When smithery scans Maestro, it will discover these 11 tools organized into four tiers:

🎭 Tier 1: Central Orchestration (1 Tool)

The main tool most users will use

1. maestro_orchestrate ⭐ [PRIMARY TOOL]

Purpose: Central orchestration engine that handles any development task
What it does: Automatically discovers your tools, analyzes the task, generates a workflow, and provides explicit execution guidance
Enhanced Features: Integrates with intelligence amplification engines for specialized processing
Usage: maestro_orchestrate({"task": "debug this error and implement a fix", "context": {...}})
Best for: Any development task - debugging, implementation, testing, documentation
Output: Complete orchestrated workflow with tool-specific commands and examples

🧠 Tier 2: Intelligence Amplification (2 Tools)

Specialized engines for enhanced capabilities

2. amplify_capability 🚀 [NEW ENGINE TOOL]

Purpose: Direct access to specialized intelligence amplification engines
What it does: Enhances specific capabilities using math, grammar, citation, code analysis engines
Available Engines: Mathematics, Grammar, APA Citation, Code Quality, Language Enhancement, Data Analysis, Web Verification
Usage: amplify_capability({"capability": "grammar_checking", "input_data": "text to analyze"})
Best for: Specialized processing that requires domain expertise beyond base LLM
Output: Precise, verified results with confidence scores and recommendations

3. get_available_engines 🔧 [ENGINE STATUS TOOL]

Purpose: Lists all available intelligence amplification engines and their status
What it does: Shows engine capabilities, dependencies, and availability
Usage: get_available_engines({})
Best for: Understanding what specialized processing capabilities are available
Output: Comprehensive engine inventory with status and capabilities

🧠 Tier 3: Context-Aware Intelligence (3 Tools)

Enhanced tools with dynamic tool discovery

4. analyze_task_with_context

Purpose: Enhanced task analysis with real-time tool discovery
What it does: Analyzes your task while discovering available MCP tools and IDE capabilities
Enhanced Features: Can leverage intelligence amplification engines for complex analysis
Usage: analyze_task_with_context({"task_description": "create a REST API", "detail_level": "comprehensive"})
Best for: Getting detailed analysis with tool ecosystem awareness
Output: Task analysis + available tool mappings + enhanced system prompts

5. create_tool_aware_execution_plan

Purpose: Create execution plans with explicit tool mappings
What it does: Generates step-by-step plans with specific tool commands, examples, and prerequisites
Enhanced Features: Includes engine amplification steps when specialized processing is needed
Usage: create_tool_aware_execution_plan({"task_description": "implement authentication", "phase_focus": "implementation"})
Best for: Getting explicit tool usage instructions for complex workflows
Output: Detailed execution plan with exact commands and tool mappings

6. get_available_tools_with_context

Purpose: Dynamic tool discovery and inventory
What it does: Scans your environment for MCP servers, tools, and IDE capabilities
Enhanced Features: Also discovers and catalogs intelligence amplification engines
Usage: get_available_tools_with_context({})
Best for: Understanding what tools are available in your development environment
Output: Comprehensive tool inventory organized by capability

📚 Tier 4: Foundation Tools (5 Tools)

Basic orchestration tools for simple workflows

7. analyze_task_for_planning

Purpose: Basic task analysis and planning
What it does: Analyzes task requirements and selects appropriate workflow templates
Usage: analyze_task_for_planning({"task_description": "build a web app", "detail_level": "balanced"})
Best for: Simple task analysis without tool discovery
Output: Task type, complexity assessment, and workflow template selection

8. create_execution_plan

Purpose: Generic execution planning
What it does: Creates step-by-step execution plans without tool mapping
Usage: create_execution_plan({"task_description": "deploy application", "phase_focus": "Deployment"})
Best for: Basic workflow planning
Output: Generic execution sequence and success criteria

9. get_available_templates

Purpose: List workflow templates
What it does: Returns available workflow templates for different task types
Usage: get_available_templates({})
Best for: Exploring available workflow approaches
Output: List of template names (e.g., "Bug Fix Development", "Feature Development")

10. get_template_details

Purpose: Template information retrieval
What it does: Provides detailed information about specific workflow templates
Usage: get_template_details({"template_name": "testing_development"})
Best for: Understanding how specific templates work
Output: Template phases, system prompts, and quality standards

🔥 NEW: Modular Intelligence Amplification Engines

🧠 What Are Intelligence Amplification Engines?

Maestro includes 7 specialized engines that amplify LLM capabilities beyond base model performance:

🔢 Mathematics Engine - Advanced mathematical computation using SymPy, NumPy, SciPy
📝 Grammar Engine - Grammar checking and writing enhancement using LanguageTool
📚 APA Citation Engine - APA 7th edition citation formatting and validation
⚡ Code Quality Engine - Code analysis, review, and improvement
💬 Language Enhancement Engine - Text improvement and clarity enhancement
📊 Data Analysis Engine - Statistical analysis and pattern recognition
🌐 Web Verification Engine - HTML analysis and accessibility checking

🎯 How Engines Integrate with Orchestration

Automatic Integration (Recommended)

// The maestro_orchestrate tool automatically uses engines when needed
{
  "tool": "maestro_orchestrate",
  "arguments": {
    "task": "proofread and improve this academic paper with proper APA citations",
    "context": {
      "document_type": "academic",
      "citation_style": "apa"
    }
  }
}

What happens internally:

🎭 Maestro analyzes the task and detects need for grammar and citation processing
📝 Grammar Engine analyzes writing quality and suggests improvements
📚 APA Citation Engine validates and formats citations
🎼 Orchestrator combines results into comprehensive workflow guidance

Direct Engine Access (Power Users)

// Use specific engines directly for specialized processing
{
  "tool": "amplify_capability",
  "arguments": {
    "capability": "grammar_checking",
    "input_data": "This sentence has some issues with grammer and style.",
    "context": {
      "analysis_type": "comprehensive",
      "document_type": "academic"
    }
  }
}

Result:

Corrected text with specific grammar fixes
Style improvement suggestions
Readability score and grade level
Confidence score and processing time

🔧 Engine Capabilities Matrix

Engine	Capabilities	Use Cases	Dependencies
Mathematics	equation_solving, calculation, statistical_analysis	Math problems, data calculations	sympy, numpy, scipy
Grammar	grammar_checking, style_analysis, writing_enhancement	Document editing, proofreading	language-tool-python, textstat
APA Citation	citation_formatting, bibliography_generation, validation	Academic writing, research papers	requests, beautifulsoup4
Code Quality	code_review, syntax_validation, security_analysis	Code improvement, debugging	ast, black
Language	text_improvement, clarity_enhancement, readability	General writing enhancement	spacy, textstat
Data Analysis	pattern_recognition, trend_analysis, outlier_detection	Data science, analytics	pandas, numpy, matplotlib
Web Verification	html_analysis, accessibility_check, seo_analysis	Web development, compliance	beautifulsoup4, requests

🎼 How the Tools Work Together in Harmony

🎭 The Enhanced Orchestrated Approach (Recommended)

User Request → maestro_orchestrate → [Internal Symphony] → Enhanced Workflow Guidance
                                   ↓
                    ┌─────────────────┴─────────────────┐
                    │                                   │
              Tool Discovery                    Intelligence Amplification
                    │                                   │
            ┌───────┴───────┐                  ┌────────┴────────┐
        MCP Servers    IDE Tools          Math    Grammar    Citation
            │              │               │        │          │
        File Ops      Code Exec           Calc   Proofread    APA
        Testing       Debug Tools         Stats   Style      Validate

When you use maestro_orchestrate, it conducts an enhanced symphony:

🔍 Discovery Phase: Scans for MCP tools AND intelligence amplification engines
🧠 Analysis Phase: Uses context-aware analysis WITH engine amplification when needed
📋 Planning Phase: Creates explicit guidance that includes both tool commands AND engine enhancement steps
⚡ Integration: Seamlessly weaves together standard tools and specialized engines

Example Enhanced Output:

Step 1: filesystem_read_file({'path': './draft.md'}) # Read document
Step 2: amplify_capability({'capability': 'grammar_checking', 'input_data': '...'}) # Enhance writing
Step 3: amplify_capability({'capability': 'apa_citation', 'input_data': '...'}) # Validate citations  
Step 4: filesystem_write_file({'path': './improved.md', 'content': '...'}) # Save improvements

🔧 The Individual Tool + Engine Approach (Power Users)

Step 1: get_available_engines() → See what engines are available
Step 2: get_available_tools_with_context() → See what tools are available  
Step 3: amplify_capability() → Use specific engine for specialized processing
Step 4: create_tool_aware_execution_plan() → Get explicit instructions
Step 5: Execute with full context and enhanced capabilities

📖 The Template + Engine Approach (Structured Workflows)

Step 1: get_available_templates() → Browse templates
Step 2: get_template_details() → Study template structure
Step 3: analyze_task_for_planning() → Apply template analysis
Step 4: create_execution_plan() → Get template-based plan (includes engine usage)

🚀 Enhanced Usage Examples

🎭 Simple Orchestrated Usage (90% of users):

{
  "tool": "maestro_orchestrate",
  "arguments": {
    "task": "Create a research paper outline with proper APA citations and check grammar",
    "context": {
      "document_type": "academic",
      "citation_style": "apa",
      "topic": "artificial intelligence ethics"
    }
  }
}

Enhanced Result: Complete workflow that automatically uses Grammar Engine for writing quality and APA Citation Engine for proper formatting.

🧠 Direct Engine Usage (Specialized Processing):

// Grammar checking with detailed analysis
{
  "tool": "amplify_capability",
  "arguments": {
    "capability": "grammar_checking",
    "input_data": "This research demonstrates how AI systems can be bias and problematic in there decision making.",
    "context": {
      "analysis_type": "comprehensive",
      "document_type": "academic"
    }
  }
}

// Mathematical problem solving
{
  "tool": "amplify_capability", 
  "arguments": {
    "capability": "equation_solving",
    "input_data": "solve for x: 2x^2 + 5x - 3 = 0"
  }
}

// APA citation validation
{
  "tool": "amplify_capability",
  "arguments": {
    "capability": "apa_citation",
    "input_data": "Smith, John (2023) AI Ethics Journal vol 10 pp 15-30",
    "context": {
      "task_type": "validate"
    }
  }
}

🔧 Engine Discovery Usage:

// See what engines are available
{
  "tool": "get_available_engines",
  "arguments": {}
}

Result: Complete status report showing which engines are available, their capabilities, dependencies, and current operational status.

🎯 When to Use Which Tool

Scenario	Recommended Tool	Why	Engine Integration
Any development task	`maestro_orchestrate`	One-stop orchestration with full intelligence	✅ Automatic engine usage
Math problems	`amplify_capability` (mathematical_reasoning)	Precise computation with verification	🔢 Mathematics Engine
Writing improvement	`amplify_capability` (grammar_checking)	Professional grammar and style analysis	📝 Grammar Engine
Academic citations	`amplify_capability` (apa_citation)	Proper APA 7th edition formatting	📚 APA Citation Engine
Code review	`amplify_capability` (code_analysis)	In-depth code quality analysis	⚡ Code Quality Engine
Need to see engines	`get_available_engines`	Real-time engine discovery and status	🔧 Engine Inventory
Complex task analysis	`analyze_task_with_context`	Enhanced analysis with engine awareness	🧠 Context + Engines
Need explicit commands	`create_tool_aware_execution_plan`	Step-by-step guidance with engine steps	📋 Tools + Engines

🏗️ Enhanced Architecture & Intelligence

🔍 Dynamic Discovery Engine

Scans Claude Desktop configuration for MCP servers
Detects IDE capabilities (Cursor, VS Code, etc.)
NEW: Discovers and catalogs intelligence amplification engines
Maintains real-time tool AND engine inventory with intelligent caching
Maps tools and engines to functional capabilities

🗺️ Intelligent Workflow Mapper

Maps discovered tools to workflow phases (Analysis, Implementation, Testing, etc.)
NEW: Integrates engine capabilities into workflow planning
Generates explicit usage instructions with commands and examples
NEW: Includes engine amplification steps with confidence scores
Assigns tool priorities (primary, secondary, fallback) AND engine priorities

🎭 Context-Aware Orchestrator

Combines tool discovery with workflow planning
NEW: Automatically detects when engine amplification would be beneficial
Provides tool-aware task analysis WITH engine enhancement
NEW: Generates hybrid execution plans (tools + engines)
Creates explicit tool execution plans with fallback strategies

🧠 Intelligence Amplification System

NEW: 7 specialized engines for domain-specific enhancement
NEW: Automatic capability routing to appropriate engines
NEW: Confidence scoring and quality verification
NEW: Seamless integration with orchestration workflow

🎪 What Makes Enhanced Maestro Special

🔄 Adaptive Intelligence

Discovers your actual tool ecosystem AND available engines
Adapts workflows to your specific capabilities AND domain expertise needs
Provides explicit commands for both tools AND specialized processing

🎯 Explicit + Enhanced Guidance

Commands: Exact tool usage syntax PLUS engine amplification calls
Examples: Real-world usage patterns WITH engine integration
Prerequisites: What needs to be ready first (tools AND engines)
Expected Output: What results to expect (enhanced with confidence scores)

⚡ Performance + Intelligence Optimized

Sub-2-second tool discovery times (tools + engines)
5-minute intelligent caching for optimal performance
NEW: Engine-specific optimization and fallback handling
Robust error handling and recovery for both tools and engines

🌟 Enhanced Use Cases

Task Type	Example Command	Engines Used
Academic Writing	`maestro_orchestrate({"task": "write research paper with APA citations and grammar checking"})`	Grammar + APA Citation
Math Problem Solving	`amplify_capability({"capability": "equation_solving", "input_data": "solve integral"})`	Mathematics
Code Quality Review	`maestro_orchestrate({"task": "review this code for quality and security issues"})`	Code Quality
Data Analysis	`amplify_capability({"capability": "statistical_analysis", "input_data": "dataset.csv"})`	Data Analysis
Document Proofreading	`amplify_capability({"capability": "grammar_checking", "input_data": "document text"})`	Grammar
Citation Formatting	`amplify_capability({"capability": "apa_citation", "input_data": "bibliography"})`	APA Citation
Web Accessibility	`amplify_capability({"capability": "accessibility_check", "input_data": "HTML code"})`	Web Verification

📦 Installation & Setup

1. Clone Repository

git clone https://github.com/tanukimcp/maestro.git
cd maestro
pip install -r requirements.txt

2. Install Engine Dependencies (Optional but Recommended)

# For Grammar Engine
pip install language-tool-python textstat

# For APA Citation Engine  
pip install requests beautifulsoup4

# For Mathematics Engine
pip install sympy numpy scipy

# For Data Analysis Engine
pip install pandas matplotlib

# For Language Enhancement Engine
pip install spacy nltk

# For Web Verification Engine
pip install requests beautifulsoup4

3. Configure Claude Desktop

Add to your Claude Desktop config file:

Windows: %APPDATA%\Claude\claude_desktop_config.json macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Linux: ~/.config/claude/claude_desktop_config.json

{
  "mcpServers": {
    "maestro": {
      "command": "python",
      "args": ["C:\\path\\to\\maestro\\src\\main.py"],
      "description": "Maestro - Intelligent Workflow Orchestration with Engine Amplification"
    }
  }
}

4. Verify Installation

Restart Claude Desktop. You should see all 11 Maestro tools available in your tool catalog, including the new engine amplification tools.

🎭 Summary

Maestro provides 11 specialized tools that transform LLM capabilities in IDEs:

1 Central Tool (maestro_orchestrate) for complete orchestration
2 Engine Tools (amplify_capability, get_available_engines) for specialized processing
3 Context-Aware Tools for intelligent analysis and planning
5 Foundation Tools for basic workflows and templates

Enhanced with 7 Intelligence Amplification Engines:

🔢 Mathematics, 📝 Grammar, 📚 APA Citation, ⚡ Code Quality, 💬 Language, 📊 Data Analysis, 🌐 Web Verification

Whether you use one tool or all eleven, Maestro adapts to your environment, provides explicit guidance, and enhances capabilities with specialized intelligence that goes far beyond base LLM performance.

"The conductor who knows every instrument in the orchestra AND has master soloists for specialized performances can compose symphonies that elevate the entire ensemble." 🎭✨

tanuki-maestro-mcp