Ask Questions in Your Language, Get Answers From Your Infrastructure

MCP is an open protocol enabling AI assistants to access external data sources through standardized interfaces. For infrastructure monitoring, MCP transforms AI assistants from generic chatbots into infrastructure experts - they can query real-time metrics, analyze ML-detected anomalies, search logs, and access live system state. Netdata’s built-in MCP server eliminates the gap between asking a question and getting an answer from your infrastructure.

Netdata provides MCP via Netdata Cloud for infrastructure-wide access (Business/Homelab plan) and as a built-in capability in every Agent and Parent (v2.6.0+, free and open-source) - no external server installation, no configuration required. Competitors require separate MCP server deployment, extensive configuration, or are in preview/beta status. Netdata’s MCP includes unique features competitors don’t offer: real-time ML anomaly detection embedded in every metric sample, direct logs access without pipelines, and console replacement capabilities (live processes, network connections, services).

All MCP-compatible clients work with Netdata: Claude Desktop, Claude Code, Cursor IDE, ChatGPT (via Cursor), Gemini CLI, VS Code (with MCP extensions), JetBrains IDEs, and any custom client using MCP protocol. Netdata supports multiple transports (streamable HTTP, SSE, WebSocket, stdio with a bridge) for maximum compatibility. Setup takes under 5 minutes - install Netdata, grab the auto-generated API key, add to AI assistant config.

Netdata provides MCP access in two ways: (1) Netdata Cloud MCP at app.netdata.cloud/api/v1/mcp - infrastructure-wide access to all your nodes, requires Business or Homelab plan, zero local setup needed. (2) Agent/Parent MCP - direct local access to individual Agents or Parents (v2.6.0+), free and open-source, requires API key from the local Netdata instance. Both options work with all MCP-compatible clients (Claude, Cursor, ChatGPT, Gemini, VS Code, etc.).

MCP tools provide comprehensive access: discover nodes/metrics/functions, query time-series with ML anomaly rates, find correlated/anomalous/unstable metrics for root cause analysis, list/query alerts with complete transition history, execute live functions (processes, network connections, systemd-journal, Windows events), and access complete logs without pipelines. AI assistants get same capabilities human users have, plus natural language interface.

Agent/Parent MCP is included free in all Netdata editions, including the open-source Community plan. Cloud MCP is available with Business or Homelab plans, providing infrastructure-wide access with zero local setup. Both include unlimited MCP queries with predictable per-node pricing. No per-query charges, no data volume fees, no feature gating. This is fundamentally different from competitors who charge based on data volume or queries - Netdata’s flat per-node pricing encourages unlimited AI usage.

Yes - designed with security-first architecture: read-only access model (no configuration changes possible), Bearer token authentication (API key required), claimed-agent requirement (production use requires Netdata Cloud authorization), no sensitive data exposure (passwords, secrets, API keys protected), complete audit trail (all queries logged), and data sovereignty (metrics/logs stay on-premises). SOC 2 Type 2 certified with GDPR/HIPAA alignment by design.

Same way Netdata scales - through distributed architecture. Cloud MCP provides infrastructure-wide queries across all your nodes from a single endpoint. Alternatively, query single Agent for single-node data or Parent for aggregated multi-node data. AI assistants see unified namespace regardless of infrastructure size (1 to 100,000+ nodes). No architectural changes required as you scale - same MCP interface works at any size.

Two distinct but complementary capabilities: MCP Server enables ANY MCP-compatible AI assistant to query Netdata data - available via Netdata Cloud for infrastructure-wide access (Business/Homelab plan) and on every Agent/Parent for local access (free, open-source). Bring your own LLM (Claude, ChatGPT, Gemini, etc.). Netdata AI Features are managed AI services (AI Insights, AI Chat, AI Troubleshoot) using Netdata-optimized playbooks - requires Netdata Cloud plus AI credits (10 free/month in Business plan). Many customers use both: MCP for ad-hoc developer troubleshooting, AI features for automated reports and management visibility.

Yes - Agent/Parent MCP servers work completely offline. AI assistants connect directly to local Netdata instances via streamable HTTP, SSE, or WebSocket. For multi-node visibility in air-gapped environments, deploy on-premises Netdata Cloud (which also provides Cloud MCP) or use Netdata Parents. No internet connectivity required for Agent/Parent MCP - all queries execute locally against edge-native data.

Two paths: Cloud MCP (simplest) - Log into Netdata Cloud, generate an API token, configure your AI assistant with endpoint https://app.netdata.cloud/api/v1/mcp - instant access to your entire infrastructure. Agent/Parent MCP - Install Netdata Agent/Parent (already includes MCP server), get API key from the configuration directory, add to AI assistant config with endpoint http://your-netdata:19999/mcp. Both paths take under 5 minutes. See detailed setup guide for specific AI assistant configurations.

APIs are designed for machines, not AI reasoning. MCP provides semantic context - AI understands what metrics mean, not just their values. Netdata’s MCP includes ML anomaly detection in every sample, so AI distinguishes normal from abnormal behavior automatically. Traditional APIs require AI to interpret raw values without context, leading to incorrect conclusions. MCP also provides standardized tool discovery - AI assistants automatically understand available capabilities without custom integration code.

Anomaly detection embedded in every metric sample transforms AI understanding. Instead of seeing just values, AI sees [value, anomaly_rate_%, annotations] - understanding which metrics are behaving abnormally without manual threshold definitions. 18 models per metric train continuously with 99% false positive reduction through consensus. This enables AI to provide accurate root cause analysis in the top 30-50 correlated metrics, identify cascading failures, and suggest remediation based on actual abnormal behavior - not just threshold violations.

Yes - Netdata MCP server handles concurrent connections from multiple AI assistants without conflicts. Each connection is independent with separate authentication. Multiple team members can use different AI assistants (Claude, Cursor, ChatGPT, etc.) simultaneously without performance degradation. This enables team collaboration where different engineers use their preferred AI tools while accessing the same infrastructure data.

Valid option for metrics-only access, but Netdata provides significantly more: ML anomaly detection (real-time, 18 models/metric, embedded in samples - Prometheus doesn’t have), logs access (direct systemd-journal/Windows Event Log querying - Prometheus doesn’t do logs), console functions (live processes, network connections, services - Prometheus doesn’t provide), zero configuration (built-in MCP server - Prometheus requires external MCP server setup), automated dashboards (algorithmic organization - Prometheus requires Grafana setup), and enterprise support (official product with SOC 2 Type 2 certification - community MCP servers unsupported).

Five common mistakes: (1) Not claiming agents - production use requires claiming for authorization, (2) Forgetting API key security - treat API keys as credentials, don’t commit to git, (3) Overwhelming AI with broad queries - start specific (‘show CPU on node-x’) before broad (‘analyze entire infrastructure’), (4) Ignoring anomaly context - train team to ask AI about anomaly rates, not just values, (5) Not using specialized tools - use find_correlated_metrics for root cause analysis instead of manual correlation. Following best practices accelerates adoption and maximizes value.

Effective query patterns: Discovery first (‘List all nodes’ → ‘Show metrics for node-x’ → ‘Query CPU metrics’), Use context (‘Show anomalous metrics in last hour on production nodes’), Use ML (‘Find metrics with highest anomaly rates during incident at 2pm’), Combine data types (‘Show logs for services with raised alerts’), Time-bound queries (always specify time windows for metric queries), Filter appropriately (use labels, patterns to narrow large infrastructures). These patterns help AI assistants provide accurate, actionable insights.

No - Netdata’s MCP server provides data access only. YOUR chosen AI assistant (Claude, ChatGPT, Gemini, etc.) sends queries to Netdata and receives responses. What the AI provider does with that data depends on your agreement with them (many offer enterprise plans with no data retention). Netdata itself never sends your observability data to AI providers. This architecture maintains complete control over data sharing - you choose which AI provider to trust.

Yes - all MCP queries are logged with timestamps, source IPs, authenticated users, and queries executed. Netdata Cloud provides centralized audit logs across all Agents/Parents. For compliance, export audit logs to SIEM systems. API key-based authentication enables tracking which AI assistants/users accessed what data when. This complete audit trail satisfies regulatory requirements and enables security team oversight of AI assistant usage.

Negligible - MCP server adds approximately 5KB RAM per active session and less than 0.1% CPU. Netdata’s architecture processes queries from existing in-memory data structures without additional database load. Even with multiple AI assistants querying simultaneously, impact remains minimal due to efficient C implementation and read-only access model. The University of Amsterdam study validated Netdata as the most energy-efficient monitoring solution - MCP capability maintains this efficiency.