# Observability MCP Server

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> 📖 **[Installation Guide](INSTALL.md)** · **[PRD](docs/PRD.md)** · **[Changelog](CHANGELOG.md)** · **[FastMCP status](docs/FASTMCP_STATUS.md)**

**FastMCP 3.3+ control plane for unified fleet monitoring (Grafana / Prometheus / Loki on port 12000)**

[![FastMCP](https://img.shields.io/badge/FastMCP-3.3+-blue.svg)](https://github.com/jlowin/fastmcp)
[![OpenTelemetry](https://img.shields.io/badge/OpenTelemetry-Enabled-green.svg)](https://opentelemetry.io)
[![Prometheus](https://img.shields.io/badge/Prometheus-Ready-orange.svg)](https://prometheus.io)
[![Grafana](https://img.shields.io/badge/Grafana-Dashboards-blue.svg)](https://grafana.com)
[![Loki](https://img.shields.io/badge/Loki-Logs-green.svg)](https://grafana.com/oss/loki/)
[![GitHub](https://img.shields.io/badge/GitHub-sandraschi/observability--mcp-blue)](https://github.com/sandraschi/observability-mcp)

MCP + web UI for the **unified** observability stack in `mcp-central-docs/monitoring` (host **12000–12006**). Agents get Grafana/Loki/Prometheus tools; humans get a **Charts** dashboard picker at **:12008** that explains boards in plain language. See [MONITORING_CURRENT_SETUP](https://github.com/sandraschi/mcp-central-docs/blob/main/monitoring/MONITORING_CURRENT_SETUP.md).

---

## Quick Start

```powershell
git clone https://github.com/sandraschi/observability-mcp
cd observability-mcp
just
```

This opens an interactive dashboard showing all available commands. Run `just bootstrap` to install dependencies, then `just serve` or `just dev` to start.

### Manual Setup

If you don't have `just` installed:

##  Features

### **FastMCP 3.3+ Integration**
-  **OpenTelemetry Integration** - Distributed tracing and metrics collection
-  **Enhanced Storage Backend** - Persistent metrics and historical data
-  **Production-Ready** - Built for high-performance monitoring

### **Comprehensive Monitoring**
-  **Real-time Health Checks** - Monitor MCP server availability and response times
-  **Performance Metrics** - CPU, memory, disk, and network monitoring with Prometheus
-  **Distributed Tracing** - Track interactions across MCP server ecosystems
-  **Centralized Logging** - Loki-powered log aggregation and querying
-  **Intelligent Alerting** - Anomaly detection and automated alerts
-  **Performance Reports** - Automated analysis and optimization recommendations

### **Advanced Analytics**
-  **Usage Pattern Analysis** - Understand how MCP servers are being used
-  **Trend Detection** - Identify performance trends and bottlenecks
-  **Log Correlation** - Correlate metrics with Loki logs for root cause analysis
-  **Optimization Insights** - Data-driven recommendations for improvement
-  **Multi-Format Export** - Prometheus, Loki, OpenTelemetry, and JSON export

---

##  Installation

### Prerequisites
- [uv](https://docs.astral.sh/uv/) installed (RECOMMENDED)
- Python 3.12+

###  Quick Start
Run immediately via `uvx`:
```bash
uvx observability-mcp
```

###  Claude Desktop Integration
Add to your `claude_desktop_config.json`:
```json
"mcpServers": {
  "observability-mcp": {
    "command": "uv",
    "args": ["--directory", "D:/Dev/repos/observability-mcp", "run", "observability-mcp"]
  }
}
```
### Prerequisites
- Python 3.11+
- FastMCP 3.3++ (automatically installed)

### Install from Source
```bash
git clone https://github.com/sandraschi/observability-mcp
cd observability-mcp
uv pip install -e .
```

##  Installation

### Prerequisites
- [uv](https://docs.astral.sh/uv/) installed (RECOMMENDED)
- Python 3.12+

###  Quick Start
Run immediately via `uvx`:
```bash
uvx observability-mcp
```

###  Claude Desktop Integration
Add to your `claude_desktop_config.json`:
```json
"mcpServers": {
  "observability-mcp": {
    "command": "uv",
    "args": ["--directory", "D:/Dev/repos/observability-mcp", "run", "observability-mcp"]
  }
}
```
##  Quick Start

### 1. Start the Server
```bash
# Using the CLI
observability-mcp run

# Or directly with Python
python -m observability_mcp.server
```

##  Installation

### Prerequisites
- [uv](https://docs.astral.sh/uv/) installed (RECOMMENDED)
- Python 3.12+

###  Quick Start
Run immediately via `uvx`:
```bash
uvx observability-mcp
```

###  Claude Desktop Integration
Add to your `claude_desktop_config.json`:
```json
"mcpServers": {
  "observability-mcp": {
    "command": "uv",
    "args": ["--directory", "D:/Dev/repos/observability-mcp", "run", "observability-mcp"]
  }
}
```
### 3. Configure Claude Desktop
Add to your `claude_desktop_config.json`:
```json
{
  "mcpServers": {
    "observability": {
      "command": "observability-mcp",
      "args": ["run"]
    }
  }
}
```

---

##  Available Tools

###  Health Monitoring
- **`monitor_server_health`** - Real-time health checks with OpenTelemetry metrics
- **`monitor_system_resources`** - Comprehensive system resource monitoring

###  Performance Analysis
- **`collect_performance_metrics`** - CPU, memory, disk, and network metrics
- **`generate_performance_reports`** - Automated performance analysis and recommendations
- **`analyze_mcp_interactions`** - Usage pattern analysis and optimization insights

###  Log Management & Loki Integration
- **`send_logs_to_loki`** - Send custom log entries to Loki for centralized aggregation
- **`query_loki_logs`** - Query logs from Loki with advanced LogQL filtering
- **`analyze_log_patterns`** - Analyze log patterns, anomalies, and trends
- **`correlate_logs_and_metrics`** - Correlate Loki logs with Prometheus metrics

###  Alerting & Anomaly Detection
- **`alert_on_anomalies`** - Intelligent anomaly detection and alerting
- **`trace_mcp_calls`** - Distributed tracing for MCP server interactions

###  Data Export
- **`export_metrics`** - Export metrics in Prometheus, OpenTelemetry, or JSON formats

---

##  Configuration

### Environment Variables
```bash
# Prometheus metrics server port
PROMETHEUS_PORT=12009

# Loki configuration
LOKI_URL=http://127.0.0.1:12002
LOG_FILE=/tmp/observability-mcp.log

# OpenTelemetry service name
OTEL_SERVICE_NAME=observability-mcp

# OTLP exporter endpoint (optional)
OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317

# Metrics retention period (days)
METRICS_RETENTION_DAYS=30
```

### Alert Configuration
The server comes with pre-configured alerts for common issues:

- **CPU Usage > 90%** (Warning)
- **Memory Usage > 1GB** (Error)
- **Error Rate > 5%** (Error)

Alerts are stored persistently and can be customized through the MCP tools.

---

##  Monitoring Dashboard

### Prometheus Metrics
Access metrics at: `http://127.0.0.1:12009/metrics`

Available metrics:
```
# Health checks
mcp_health_checks_total{status="healthy|degraded|unhealthy", service="..."} 1

# Performance metrics
mcp_performance_metrics_collected{service="..."} 1

# System resources
mcp_cpu_usage_percent{} 45.2
mcp_memory_usage_mb{} 1024.5

# Traces and alerts
mcp_traces_created{service="...", operation="..."} 1
mcp_alerts_triggered{type="active|anomaly"} 1
```

### Integration with Grafana & Loki
**Grafana Dashboards are State-of-the-Art for Observability**

1. **Add Data Sources in Grafana:**
   - Use unified stack: Grafana http://127.0.0.1:12000, Prometheus http://127.0.0.1:12001, Loki http://127.0.0.1:12002

2. **Import Dashboards:**
   - Import the provided `mcp-observability.json` dashboard
   - Customize panels for your specific MCP ecosystem

3. **Log Integration:**
   - Query logs with Loki: `{job="observability-mcp"} |= "ERROR"`
   - Correlate metrics with logs for comprehensive troubleshooting

**Why Grafana + Loki = SOTA Observability:**
- **Unified View**: Single pane of glass for metrics, logs, and traces
- **Powerful Queries**: PromQL + LogQL for complex analysis
- **Rich Visualizations**: State-of-the-art dashboards with real-time updates
- **Alert Integration**: Native alerting with multiple notification channels

---

##  Architecture

### FastMCP 3.3+ Features Leveraged

#### **OpenTelemetry Integration**
- **Distributed Tracing**: Track requests across multiple MCP servers
- **Metrics Collection**: Structured performance data collection
- **Context Propagation**: Maintain context across service boundaries

#### **Enhanced Persistent Storage**
- **Historical Data**: Store metrics and traces for trend analysis
- **Cross-Session Persistence**: Data survives server restarts
- **Efficient Storage**: Optimized for time-series data

#### **Production Architecture**
```
        
   MCP Servers    Observability      Prometheus     
   (Monitored)          MCP Server            Metrics       
        
                                                       
                                                       
                          
                        Persistent             Grafana       
                         Storage               Dashboards    
                             (State-of-Art)
                                               
                 
   Application         Loki        
     Logs               Log Aggregation
    
```

---

##  Usage Examples

### Health Monitoring
```python
# Check MCP server health
result = await monitor_server_health(
    service_url="http://localhost:8000/health",
    timeout_seconds=5.0
)
print(f"Status: {result['health_check']['status']}")
```

### Performance Analysis
```python
# Collect system metrics
metrics = await collect_performance_metrics(service_name="my-mcp-server")
print(f"CPU: {metrics['metrics']['cpu_percent']}%")
print(f"Memory: {metrics['metrics']['memory_mb']} MB")
```

### Distributed Tracing
```python
# Record a trace
trace = await trace_mcp_calls(
    operation_name="process_document",
    service_name="ocr-mcp",
    duration_ms=150.5,
    attributes={"file_size": "2.3MB", "format": "PDF"}
)
```

### Generate Reports
```python
# Create performance report
report = await generate_performance_reports(
    service_name="web-mcp",
    days=7
)
print("Performance Summary:", report['summary'])
print("Recommendations:", report['recommendations'])
```

### Loki Log Management
```python
# Send custom logs to Loki
result = await send_logs_to_loki(
    log_message="User authentication failed",
    level="warning",
    labels={"service": "auth-service", "user_id": "12345"}
)

# Query logs from Loki
logs = await query_loki_logs(
    query='{job="observability-mcp"} |= "ERROR"',
    start_time="1h",
    limit=100
)

# Analyze log patterns
patterns = await analyze_log_patterns(
    query='{service="web-mcp"}',
    time_window="24h",
    min_occurrences=10
)

# Correlate logs with metrics
correlation = await correlate_logs_and_metrics(
    log_query='{service="api"} |= "timeout"',
    metric_query="rate(http_requests_total{status='500'}[5m])",
    time_window="1h"
)
```

---

##  Development

### Running Tests
```bash
# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run with coverage
pytest --cov=observability_mcp --cov-report=html
```

### Code Quality
```bash
# Format code
black src/

# Lint code
ruff check src/

# Type checking
mypy src/
```

### Docker Development
```bash
# Build development image
docker build -t observability-mcp:dev -f Dockerfile.dev .

# Run with hot reload
docker run -p 12009:12009 -e PROMETHEUS_PORT=12009 -v $(pwd):/app observability-mcp:dev
```

---

##  Performance Benchmarks

### FastMCP 3.3+ Benefits
- **OpenTelemetry Overhead**: <1ms per trace
- **Storage Performance**: 1000+ metrics/second
- **Memory Usage**: 50MB baseline + 10MB per monitored service
- **Concurrent Monitoring**: 100+ services simultaneously

### Recommended Hardware
- **CPU**: 2+ cores for metrics processing
- **RAM**: 2GB minimum, 4GB recommended
- **Storage**: 10GB for metrics history (30 days retention)

---

##  Troubleshooting

### Common Issues

#### Server Won't Start
```bash
# Check Python version
python --version  # Should be 3.11+

# Check FastMCP installation
pip show fastmcp  # Should be 2.14.1+

# Check dependencies
pip check
```

#### Metrics Not Appearing
```bash
# Check Prometheus endpoint
curl http://127.0.0.1:12009/metrics

# Verify OpenTelemetry configuration
observability-mcp metrics
```

#### High Memory Usage
- Reduce `METRICS_RETENTION_DAYS`
- Implement metric aggregation
- Monitor with `monitor_system_resources`

#### Storage Issues
- Check available disk space
- Clean old metrics: `rm -rf ~/.observability-mcp/metrics/*`
- Restart server to recreate storage

---

##  Contributing

### Development Setup
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Submit a pull request

### Code Standards
- **FastMCP 3.3++**: Use latest features and patterns
- **OpenTelemetry**: Follow OTEL  practices
- **Async First**: All operations should be async
- **Type Hints**: Full type coverage required
- **Documentation**: Comprehensive docstrings

### Testing Strategy
- **Unit Tests**: Core functionality
- **Integration Tests**: MCP server interactions
- **Performance Tests**: Benchmarking and load testing
- **Chaos Tests**: Failure scenario testing

---


## 🛡️ Industrial Quality Stack

This project adheres to **SOTA 14.1** industrial standards for high-fidelity agentic orchestration:

- **Python (Core)**: [Ruff](https://astral.sh/ruff) for linting and formatting. Zero-tolerance for `print` statements in core handlers (`T201`).
- **Webapp (UI)**: [Biome](https://biomejs.dev/) for sub-millisecond linting. Strict `noConsoleLog` enforcement.
- **Protocol Compliance**: Hardened `stdout/stderr` isolation to ensure crash-resistant JSON-RPC communication.
- **Automation**: [Justfile](./justfile) recipes for all fleet operations (`just lint`, `just fix`, `just dev`).
- **Security**: Automated audits via `bandit` and `safety`.

##  License

MIT License - see [LICENSE](LICENSE) file for details.

---

##  Acknowledgments

- **FastMCP Team** - For the  2.14.1 framework with OpenTelemetry integration
- **OpenTelemetry Community** - For the observability standards and tools
- **Prometheus Team** - For the metrics collection and alerting system
- **Grafana Labs** - For Loki log aggregation and Grafana's state-of-the-art dashboarding
- **Grafana Community** - For the visualization platform that powers modern observability

---

##  Related Projects

- [**FastMCP**](https://github.com/jlowin/fastmcp) - The framework this server is built on
- [**OpenTelemetry Python**](https://opentelemetry.io/docs/python/) - Observability instrumentation
- [**Prometheus**](https://prometheus.io) - Metrics collection and alerting
- [**Grafana**](https://grafana.com) - State-of-the-art dashboards and visualization
- [**Loki**](https://grafana.com/oss/loki/) - Log aggregation and querying
- [**Promtail**](https://grafana.com/docs/loki/latest/clients/promtail/) - Log shipping agent

---

**Built with  using FastMCP 3.3+, OpenTelemetry, Prometheus, Grafana & Loki - State-of-the-Art Observability**