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    Enables AI agents to interact with Freeplay, an ops platform for AI engineering teams, to analyze production logs, identify quality issues, iterate on prompts and agents using real data, and run experiments before deploying.
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    2
    Apache 2.0
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    Enables LLM clients to interact with OpenSearch Dashboards, including reading saved objects, listing tenants and index patterns, and querying logs through the Dashboards API.
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    MIT
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    Provides tools for accessing Alibaba Cloud observability products including SLS (Log Service) and ARMS (Application Real-time Monitoring Service), allowing any MCP-compatible AI assistants to quickly interact with these services.
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    eBPF-based GPU causal observability agent with MCP server. Traces CUDA Runtime and Driver APIs via kernel uprobes and host events via tracepoints to build causal chains explaining GPU latency. 7 tools: get_check, get_trace_stats, get_causal_chains, get_stacks, run_demo, get_test_report, run_sql. Telegraphic compression reduces token usage ~60%. Supports stdio and HTTPS (TLS 1.3) transport.
    Last updated
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    Enables Claude to interact with Karma Alert dashboard to monitor and analyze Kubernetes alerts. Provides tools to check alert status, filter by cluster/severity, get detailed alert information, and analyze alert statistics and trends.
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    MIT
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    A Model Context Protocol server that provides tools to interact with Matomo Analytics API, enabling management of sites, users, goals, segments, and access to analytics reports through a MCP interface.
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    ISC
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    Enables AI agents to interact seamlessly with Splunk environments through 20+ tools for search, analytics, data discovery, administration, and health monitoring. Features AI-powered troubleshooting workflows and supports multiple Splunk instances with production-ready security.
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    Apache 2.0
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    Enables AI assistants to interact with Rapid7 InsightIDR SIEM for investigating incidents, searching logs with LEQL, managing alerts and assets, analyzing user behavior, and handling threat intelligence.
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    MIT
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    A Model Context Protocol server that lets AI assistants interact with the Sentry API to retrieve and analyze error data, manage projects, and monitor application performance.
    Last updated
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    This project provides a Model Context Protocol (MCP) server that allows language models and other MCP clients to interact with a SuzieQ network observability instance via its REST API.
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    MIT