mcp-switchboard
Intelligent MCP server orchestrator that automates configuration, orchestration, and lifecycle management of other MCP servers for AI agents.
README
mcp-switchboard
Intelligent MCP server orchestrator that automates configuration, orchestration, and lifecycle management of other MCP servers for AI agents.
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:
setup_mcp_servers- Complete orchestration with real-time health monitoringanalyze_task- Extract task requirementsselect_servers- Recommend MCP servers (with historical learning)manage_servers- Subprocess managementrollback_configuration- Restore previous configlist_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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