Intelligent tool-call review system for AI agents
Superego MCP Server provides a configurable review system to AI agents to reduce the amount of manual approvals needed as well as provide automated guardrails against dangerous operations. It analyzes incoming tool calls against a set of rules and if no rule is matched it defers to another agent for review or escalation to a human.
- Rule-based interception: Define flexible rules using YAML configuration with advanced pattern matching (regex, glob, JSONPath)
- Multiple actions: Allow, block, or require approval (sampling) based on configurable policies
- Claude Code Hooks Integration: Direct integration with Claude Code for real-time security evaluation
- Multi-transport Support: STDIO, HTTP, and SSE transports for flexible deployment
- Hot reload: Configuration changes are applied without restart
- AI-powered evaluation: Optional AI inference for complex security decisions
- Performance optimized: Request batching, caching, and connection pooling
- Comprehensive monitoring: Built-in metrics, health checks, and performance dashboard
- Structured logging: Comprehensive logging with structured output
- MCP compatibility: Full Model Context Protocol support with FastMCP framework
# Install with uv (recommended)
uv pip install superego-mcp
# Or install from source
git clone https://github.com/toolprint/superego-mcp
cd superego-mcp
uv sync-
Run security evaluation (for Claude Code hooks):
echo '{"tool_name": "bash", "tool_input": {"command": "ls"}}' | superego advise
-
Start the MCP server:
# Default STDIO transport superego mcp # HTTP transport on custom port superego mcp -t http -p 9000 # With custom config superego mcp -c ~/.toolprint/superego/config.yaml
-
Run interactive demo:
just demo-fastagent-simple
Superego provides seamless integration with Claude Code through hooks:
# Add hooks for specific tools (recommended)
superego hooks add --matcher "Bash|Write|Edit|MultiEdit"
# Add universal hook for all tools
superego hooks add --matcher "*"
# Use centralized server mode
superego hooks add --matcher "*" --url http://localhost:8000For complete hook setup instructions and examples, see: Claude Code Hooks Setup Guide
{
"hooks": {
"PreToolUse": [
{
"matcher": "*",
"hooks": [
{
"type": "command",
"command": "superego advise",
"timeout": 5000
}
]
}
]
}
}# Server settings
host: "localhost"
port: 8000
debug: false
log_level: "INFO"
# Rule engine settings
rules_file: "config/rules.yaml"
hot_reload: true
# Multi-transport configuration
transport:
stdio:
enabled: true
http:
enabled: true
host: "0.0.0.0"
port: 8000
sse:
enabled: true
port: 8002
# AI inference configuration
inference:
timeout_seconds: 30
provider_preference:
- "claude_cli"
- "mcp_sampling"
cli_providers:
- name: "claude_cli"
enabled: true
type: "claude"
command: "claude"
model: "claude-sonnet-4-20250514"rules:
# Block dangerous commands
- id: "block_destructive_commands"
priority: 1
conditions:
tool_name:
type: "regex"
pattern: "^(rm|delete|remove|destroy).*"
action: "deny"
reason: "Destructive command pattern detected"
# Protect system directories
- id: "protect_system_files"
priority: 2
conditions:
parameters:
path:
type: "glob"
pattern: "/etc/**"
action: "deny"
reason: "System directory access denied"
# Require approval for file operations
- id: "sample_file_operations"
priority: 10
conditions:
AND:
- tool_name: ["edit", "write", "delete"]
- parameters:
path:
type: "regex"
pattern: "^(?!/tmp/).*$"
action: "sample"
reason: "File operation requires AI evaluation"
sampling_guidance: "Evaluate if this file operation is safe"# Setup development environment
just dev
# Run tests
just test
# Run with coverage
just test-cov
# Lint and format
just lint
just format
# Type check
just typecheck
# Run all quality checks
just checksrc/superego_mcp/
├── __init__.py # Package initialization
├── cli.py # Unified CLI interface
├── cli_eval.py # Evaluation mode implementation
├── cli_hooks.py # Claude Code hooks management
├── main.py # MCP server entry point
├── main_optimized.py # Performance-optimized server
├── stdio_main.py # STDIO transport handler
├── domain/ # Business logic and models
│ ├── models.py # Core domain models
│ ├── pattern_engine.py # Pattern matching engine
│ ├── security_policy.py # Security evaluation engine
│ ├── services.py # Domain services
│ ├── repositories.py # Domain repositories
│ ├── claude_code_models.py # Claude Code hook models
│ └── hook_integration.py # Hook integration service
├── infrastructure/ # External services and adapters
│ ├── config.py # Configuration management
│ ├── config_watcher.py # Hot reload implementation
│ ├── ai_service.py # AI inference service
│ ├── inference.py # Extensible inference system
│ ├── circuit_breaker.py # Circuit breaker pattern
│ ├── metrics.py # Prometheus metrics
│ ├── performance.py # Performance optimization
│ └── logging_config.py # Structured logging setup
└── presentation/ # API and transport layers
├── mcp_server.py # FastMCP server implementation
├── http_transport.py # HTTP/WebSocket transport
├── sse_transport.py # Server-sent events transport
├── handlers.py # Request handlers
├── monitoring.py # Monitoring dashboard
└── server.py # Transport server orchestration
# Run specific test file
just test-file tests/test_security_policy.py
# Run integration tests
uv run pytest tests/test_mcp_server_integration.py -v
# Run performance tests
just test-performance
# Run load tests
just load-test# Run optimized server
just run-optimized
# Run performance demo
just demo-performance
# Benchmark rule evaluation
just benchmark-rulessuperego advise- One-off security evaluation for Claude Code hookssuperego mcp- Launch the FastMCP serversuperego hooks- Manage Claude Code hook configurations
{
"tool_name": "string",
"tool_input": {
"parameter1": "value1",
"parameter2": "value2"
},
"session_id": "string",
"transcript_path": "string",
"cwd": "string",
"hook_event_name": "PreToolUse"
}{
"decision": "allow|deny|sample",
"confidence": 0.95,
"reasoning": "Explanation of the decision",
"risk_factors": ["risk1", "risk2"],
"matched_rules": ["rule_id1", "rule_id2"]
}Access the monitoring dashboard at http://localhost:9090/dashboard when running with metrics enabled.
Metrics available:
- Request volume by tool type
- Decision distribution (allow/deny/sample)
- Processing times
- AI inference latency
- Error rates
-
Import errors: Ensure proper Python path setup
export PYTHONPATH=$PYTHONPATH:$(pwd)/src
-
Hook timeouts: Check Superego service availability
superego mcp --debug
-
AI inference failures: Verify API keys are set
export ANTHROPIC_API_KEY=your-key-here
Enable debug logging:
superego mcp --debug- Server logs:
stderr(structured JSON format) - Hook operations:
/tmp/superego_hook.log - Metrics:
http://localhost:9090/metrics
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'feat: add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
- Follow conventional commits format
- Ensure all tests pass (
just check) - Add tests for new features
- Update documentation as needed
- Maintain type safety with mypy
MIT License - see LICENSE file for details
- Pull the latest image:
docker pull toolprint/superego-mcp:latest- Run with Docker Compose:
# Start in development mode
docker-compose up -d
# Start in production mode
docker-compose -f docker-compose.prod.yml up -d- Comprehensive container usage guide: Container Usage Docs
- Deployment guide: Deployment Guide
- Troubleshooting: Troubleshooting Guide