---
title: "Telemetry"
description: "Understanding MCP-use's telemetry system"
icon: "chart-line"
---

## Overview

MCP-use includes an **opt-out telemetry system** that helps us understand how the library is being used in practice. This data enables us to:

- **Prioritize development** based on real usage patterns
- **Optimize performance** for common workflows
- **Improve compatibility** with popular model providers
- **Focus on the most valuable features**

<Warning>
**Privacy First**: All telemetry is **anonymized** and can be **completely disabled** with a single environment variable.
</Warning>

## What We Collect

### Agent Execution Data
When you use `MCPAgent.run()` or `MCPAgent.astream()`, we collect:

- **Query and response content** (to understand use cases)
- **Model provider and name** (e.g., "openai", "gpt-4")
- **MCP servers connected** (types and count, not specific URLs/paths)
- **Tools used** (which MCP tools are popular)
- **Performance metrics** (execution time, steps taken)
- **Configuration settings** (memory enabled, max steps, etc.)

### System Information
- **Package version** (for version adoption tracking)
- **Error types** (for debugging and improvement)
- **Package download analytics** (via Scarf for distribution insights)

### What We DON'T Collect
- **Personal information** (no names, emails, or identifiers)
- **Server URLs or file paths** (only connection types)
- **API keys or credentials** (never transmitted)
- **IP addresses** (PostHog configured with `disable_geoip=False`)

## How to Disable Telemetry

### Environment Variable (Recommended)
```bash
export MCP_USE_ANONYMIZED_TELEMETRY=false
```

### In Your Code
```python
import os
os.environ["MCP_USE_ANONYMIZED_TELEMETRY"] = "false"

# Now use MCP-use normally - no telemetry will be collected
from mcp_use import MCPAgent
```

### Verification
When telemetry is disabled, you'll see this debug message:
```
DEBUG: Telemetry disabled
```

When enabled, you'll see:
```
INFO: Anonymized telemetry enabled. Set MCP_USE_ANONYMIZED_TELEMETRY=false to disable.
```

## Data Storage and Privacy

### Anonymous User IDs
- A random UUID is generated and stored locally in your cache directory
- **Linux/Unix**: `~/.cache/mcp_use/telemetry_user_id`
- **macOS**: `~/Library/Caches/mcp_use/telemetry_user_id`
- **Windows**: `%LOCALAPPDATA%\mcp_use\telemetry_user_id`

### Data Transmission
- Data is sent to both **PostHog** (EU servers: `https://eu.i.posthog.com`) and **Scarf** (`https://mcpuse.gateway.scarf.sh/simple/`)
- **No personal information** is ever transmitted
- Data is used only for **aggregate analysis** and **package usage analytics**

## Example Telemetry Event

Here's what a typical telemetry event looks like:

```json
{
  "event": "mcp_agent_execution",
  "distinct_id": "550e8400-e29b-41d4-a716-446655440000",
  "properties": {
    "mcp_use_version": "1.3.0",
    "execution_method": "run",
    "query": "Help me analyze sales data from the CSV file",
    "response": "I'll help you analyze the sales data...",
    "model_provider": "openai",
    "model_name": "gpt-4",
    "server_count": 2,
    "server_identifiers": [
      {"type": "stdio", "command": "python -m server"},
      {"type": "http", "base_url": "localhost:8080"}
    ],
    "tools_used_names": ["read_file", "analyze_data"],
    "execution_time_ms": 2500,
    "success": true
  }
}
```

## Benefits to the Community

### For Users
- **Better library** through data-driven improvements
- **Faster issue resolution** via error pattern detection
- **Feature prioritization** based on actual usage

### For Developers
- **Compatibility insights** for new model providers
- **Performance optimization** targets
- **Usage pattern understanding** for better APIs

## Technical Implementation

### Clean Architecture
The telemetry system uses a **decorator pattern** that ensures:
- **Zero overhead** when disabled
- **No exceptions** if PostHog or Scarf services are unavailable
- **Graceful degradation** in all failure scenarios
- **Dual telemetry** to both PostHog and Scarf for comprehensive analytics

### Code Example
```python
# This is how telemetry works internally:
@requires_telemetry
def track_agent_execution(self, ...):
    # This method only executes if telemetry is enabled
    # If disabled, it returns None immediately
    pass
```

## Frequently Asked Questions

### Can I see what data is being sent?
Yes! Set your logging level to DEBUG to see telemetry events:
```python
import logging
logging.basicConfig(level=logging.DEBUG)
```

### Does telemetry affect performance?
- **When disabled**: Zero performance impact
- **When enabled**: Minimal impact (async data transmission)

### Can I opt out after using the library?
Yes! Set the environment variable and restart your application. You can also delete the user ID file to reset your anonymous identifier.

### Is this GDPR compliant?
Yes. The telemetry system:
- Collects no personal data
- Uses anonymous identifiers
- Provides easy opt-out
- Processes data for legitimate business interests (software improvement)

## Support

If you have questions about telemetry:
- **Disable it**: Use `MCP_USE_ANONYMIZED_TELEMETRY=false`
- **Report issues**: [GitHub Issues](https://github.com/anthropics/mcp-use/issues)
- **Check logs**: Enable DEBUG logging to see telemetry activity

<Note>
Remember: Telemetry helps us build a better library for everyone, but **your privacy comes first**. We've designed the system to be transparent, minimal, and easily disabled.
</Note>
