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    Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.
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    MIT
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    An MCP server that provides cost and reliability observability for LLM and agent workflows. It records model calls and allows querying and aggregating telemetry data through MCP tools.
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    MIT
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    Enables AI assistants to read live macOS processes and identify monitoring software, including Chinese enterprise EDR/DLP/MDM tools, through a read-only MCP server.
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    5
    MIT
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    Live .NET runtime diagnostics for AI assistants. Ask Claude to diagnose memory leaks, GC pressure, LOH fragmentation, and thread starvation in any running .NET process — no code changes required.
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    MIT
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    MCP plugin that alerts you when AI token usage is wasteful. Fires warnings, errors, and alerts on large outputs, verbose logs, and repetitive history, auto-suppressing noise to keep your context lean.
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    MIT
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    Provides a notify_done tool for Claude Desktop to trigger desktop popup notifications when tasks complete.
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    Enables reading and analyzing Cloudflare Workers logpush data stored in R2 buckets. Supports searching logs with filters, viewing statistics, accessing errors, and browsing logs by date and environment.
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    Enables AI assistants to access Scout Monitoring performance and error data through Scout's API. Provides traces, errors, metrics, and insights for Rails, Django, FastAPI, Laravel and other applications to help identify and fix performance issues like N+1 queries, slow endpoints, and memory bloat.
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    MIT
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    Zero-config MCP server that gives AI coding assistants a real-time diagnostic snapshot of your local dev environment. Detects framework, running services, recent errors, git state, and provides a health diagnosis in one call.
    Last updated
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    MIT
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    A dependency-free MCP server for Apache Incubator PMC oversight that helps identify podlings needing attention, assess graduation readiness, and generate podling briefings by combining lifecycle data and community health signals.
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    21
    MIT
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    MCP server implementation that enables LLMs to interact with Rollbar error tracking data, allowing users to list and analyze errors, view occurrences, track deployments, and access project information.
    Last updated
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    MIT