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    Enables Claude to retrieve and analyze Google Analytics 4 data through 20+ analysis tools, providing insights on traffic, user behavior, conversions, and engagement metrics in Japanese-formatted output.
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    MIT
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    Enables querying and formatting Loki logs from Grafana via the Model Context Protocol. It supports LogQL queries, label retrieval, and provides results in text, JSON, or markdown formats.
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    MIT
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    Analyzes log files using AI-powered tools to detect errors, patterns, and anomalies through natural language queries in Cursor IDE.
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    A read-only kubectl MCP server for AI assistants. The kubectl verb is hardcoded in each tool (get/describe/logs/events) and user input only fills argument values — no mutation path even with malicious input, and Secret/ConfigMap values are never returned (metadata only). Returns structured JSON, plus one-shot k8s_triage (health scan) and k8s_inventory (cluster snapshot).
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    MIT
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    MCP server exposing rlg (RustLogs) log files as tools for on-call / SRE agent workflows. Three tools over JSON-RPC stdio — tail_log (last N events, filtered by level/target), filter_log (predicate filtering), summarize_errors (grouped error taxonomy). Pure Rust. MIT OR Apache-2.0.
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    Apache 2.0
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    MCP server that transforms raw log query results into structured incident reports (Markdown, JSON, HTML) with correlation to git commits and tickets.
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    MIT
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    GenieACS-MCP is a Go-based MCP server that bridges any GenieACS (TR-069 ACS) instance, exposing device data, firmware management, and CPE actions (reboot, parameter refresh, firmware download) over JSON-RPC
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    GPL 3.0
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    Exposes homelab and IT-ops tools to Claude, including system health monitoring, Grafana alert states, Docker container status, Loki logs, SMART disk health, and more.
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    An MCP server that exposes GPU-accelerated anomaly detection to AI assistants via the Model Context Protocol. Provides two MCP tools: waveguard_scan (send training + test data in one call, returns per-sample anomaly scores and top explanatory features) and waveguard_health (check API and GPU status). Works on time series, JSON, numbers, text, and images — fully stateless.
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    MIT
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    Enables LLMs to analyze network packet captures (PCAP files) from local or remote sources through a modular architecture. Supports DNS traffic analysis with structured JSON responses for network security and troubleshooting tasks.
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    Enables AI assistants to automatically inspect and analyze application runtime log files for debugging and troubleshooting. Supports monitoring multiple log directories simultaneously with tools for listing, reading, searching, and paginating through log files.
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    8
    MIT
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    Enables LLMs to analyze network packet captures (PCAP files) from local or remote sources. Provides structured analysis of network protocols like DNS traffic through a modular architecture using scapy.
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    8
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    MIT