mcp-switchboard
Orchestrates Atlassian MCP servers to enable AI agents to interact with Atlassian tools like Jira and Confluence.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-switchboardorchestrate MCP servers for deploying to AWS ECS"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 serverFrom 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]
This server cannot be installed
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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