mcp-switchboard

mcp-switchboard

Intelligent MCP server orchestrator that automates configuration, orchestration, and lifecycle management of other MCP servers for AI agents.

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README

mcp-switchboard

Intelligent MCP server orchestrator that automates configuration, orchestration, and lifecycle management of other MCP servers for AI agents.

Tests Coverage Python

Overview

mcp-switchboard eliminates the manual overhead of configuring MCP servers for AI agents by:

  • Analyzing task context to determine required MCP servers
  • Selecting appropriate servers based on intelligent matching
  • Configuring servers with correct credentials and settings
  • Validating server health and tool availability
  • Learning from historical patterns to improve recommendations

Time Savings: Reduces MCP setup from 5-15 minutes to <30 seconds per task

Quick Start

Installation

Using uv (Recommended - Fast & Modern):

# Install from PyPI
uv pip install mcp-switchboard

# Or run directly without installation
uvx mcp-switchboard --analyze "Deploy ECS to prod"
uvx mcp-switchboard-server  # Run MCP server

From source:

# Clone repository
git clone https://github.com/aslanpour/mcp-switchboard
cd mcp-switchboard

# Install with uv
uv pip install -e .

# Or with pip
pip install -e .

Basic Usage

from mcp_switchboard.analyzer.analyzer import TaskAnalyzer
from mcp_switchboard.selector.selector import ServerSelector
from mcp_switchboard.config.registry import ServerRegistry

# Analyze task
analyzer = TaskAnalyzer()
analysis = analyzer.analyze("Deploy ECS to prod Tokyo using DEVOPS-123")

# Select servers
registry = ServerRegistry()
selector = ServerSelector(registry)
selection = selector.select(analysis)

# Results
print(f"Selected: {[s.server_name for s in selection.selected_servers]}")
# Output: ['atlassian-mcp', 'aws-api-mcp']

Features

  • ✅ Intelligent task analysis with confidence scoring
  • ✅ Capability-based server selection
  • ✅ AWS SSO and OAuth credential management
  • ✅ Multi-agent configuration support
  • ✅ Snapshot and rollback capabilities
  • ✅ State tracking and historical learning
  • ✅ Structured logging and metrics

Requirements

  • Python 3.9+
  • AWS CLI (for AWS SSO)
  • Node.js/npm (for npm-based MCP servers)

Development

# Run tests
pytest tests/

# Format code
black src/ tests/

# Type check
mypy src/

Status

Current Version: v1.0.0 - Production Ready 🎉

Functional Completion: 100%

What Works:

  • ✅ Task analysis (keyword + LLM)
  • ✅ Server selection with confidence scoring
  • Historical pattern learning (NEW in v1.0.0)
  • ✅ Credential management (AWS SSO, OAuth, tokens)
  • ✅ Configuration writing with snapshots
  • ✅ Configuration rollback
  • Real-time health monitoring (NEW in v1.0.0)
  • ✅ State tracking and history
  • ✅ MCP server with 6 tools
  • ✅ Multi-transport (STDIO/SSE/HTTP)
  • ✅ Server subprocess management
  • ✅ Full orchestration workflow

MCP Tools Available:

  1. setup_mcp_servers - Complete orchestration with real-time health monitoring
  2. analyze_task - Extract task requirements
  3. select_servers - Recommend MCP servers (with historical learning)
  4. manage_servers - Subprocess management
  5. rollback_configuration - Restore previous config
  6. list_snapshots - View available snapshots

Advanced Features:

  • Real-time server startup validation
  • Historical pattern learning for better recommendations
  • Confidence boosting based on past success
  • Exponential backoff retry logic
  • Detailed health metrics (startup time, tools available)

Tests: 85/85 passing (100%)

Performance:

  • Task analysis: <2ms
  • Server selection: <1ms
  • Total orchestration: 2-3 seconds
  • Health validation: <1 second per server

See CHANGELOG.md for version history.

License

[To be determined]

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