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Copy file name to clipboardExpand all lines: docs/ai-and-machine-learning/mcp-integration.mdx
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:::
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:::note
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This integration leverages new and evolving AI technologies. While Netdata provides comprehensive infrastructure monitoring capabilities, the AI analysis features **depend on external AI services** and their inherent limitations. The quality and accuracy of AI-generated insights are **subject to the capabilities and constraints of the underlying AI models, not Netdata's monitoring functionality**.
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:::
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## What is MCP?
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**Model Context Protocol (MCP)** is a new open standard that allows AI assistants to connect directly to your data sources and tools. Think of it as a bridge that lets AI systems access and analyze your real-time infrastructure data instead of just providing generic advice.
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### Real-World Example
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**The Problem**: EMQX message broker goes down, affecting multiple services across your infrastructure.
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**The Problem**: Database server goes down, affecting multiple applications across your infrastructure.
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```mermaid
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flowchart TD
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A("🚨 EMQX Outage Occurs")
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A("🚨 Database Outage Occurs")
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B("📊 Traditional Approach")
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C("🤖 Netdata + MCP")
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## The Future of Infrastructure Monitoring
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With Netdata MCP, we're moving toward a future where:
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With Netdata MCP, we're moving toward a future where**AI understands your infrastructure** as well as your best engineers. **Troubleshooting becomes conversational** rather than manual chart analysis, **post-mortems write themselves** with complete root cause analysis, and **your monitoring system becomes a true team member** that helps solve problems.
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-**AI understands your infrastructure** as well as your best engineers
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-**Troubleshooting becomes conversational** rather than manual chart analysis
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-**Post-mortems write themselves** with complete root cause analysis
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-**Your monitoring system becomes a team member** that helps solve problems
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:::note
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This represents our vision for the future of infrastructure monitoring. While we're making significant progress with MCP integration, these capabilities are aspirational goals we're working toward, not current product features. For now, we are building the foundations for the reality we want to create.
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:::
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**Join us in pioneering the next generation of intelligent infrastructure monitoring.**
Copy file name to clipboardExpand all lines: docs/collecting-metrics/Hardware Devices and Sensors/S.M.A.R.T..mdx
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| scan_every | interval for discovering new devices using `smartctl --scan`, measured in seconds. Set to 0 to scan devices only once on startup. | 900 | no |
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| poll_devices_every | interval for gathering data for every device, measured in seconds. Data is cached for this interval. | 300 | no |
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| device_selector | Specifies a pattern to match the 'info name' of devices as reported by `smartctl --scan --json`. | * | no |
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| concurrent_scans | Number of devices to scan concurrently. Set to 0 for sequential scanning (default behavior). Improves performance when monitoring many devices. | 0 | no |
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| extra_devices | Allows manual specification of devices not automatically detected by `smartctl --scan`. Each device entry must include both a name and a type. See "Configuration Examples" for details. | [] | no |
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| no_check_power_mode | Skip data collection when the device is in a low-power mode. Prevents unnecessary disk spin-up. | standby | no |
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```
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</details>
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##### Concurrent scanning for multiple devices
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This example demonstrates enabling concurrent scanning to improve performance when monitoring many devices.
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<details open>
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<summary>Config</summary>
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```yaml
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jobs:
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- name: smartctl
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concurrent_scans: 4 # Scan up to 4 devices concurrently
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```
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</details>
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##### Extra devices
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This example demonstrates using `extra_devices` to manually add a storage device (`/dev/sdc`) not automatically detected by `smartctl --scan`.
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