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]
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.