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"A service for error tracking and performance monitoring" matching MCP servers:

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    Enables interaction with Google Cloud services including billing cost analysis, log querying, and metrics monitoring through natural language commands. Provides comprehensive tools for managing GCP resources, analyzing costs, detecting anomalies, and retrieving operational insights.
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    Apache 2.0
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    Provides over 1,000 creative ways to decline requests across four categories (polite, humorous, professional, and creative). The MCP server wraps a REST API to help users craft professional rejections through natural language interactions.
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    MIT
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    An MCP server that enables querying performance review periods, records, details, and saving self-assessment drafts without final submission.
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    Enables LLM agents to search and analyze Elasticsearch logs for errors, detect recurring patterns, analyze error-rate trends, and retrieve full trace context through MCP tools.
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    MIT
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    A generic MCP server for tracking any entity type (e.g., weight, workouts, books) without predefined schemas, using SQLite with JSON columns for flexible data storage and querying.
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    MIT
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    Governs and automates network monitoring across SolarWinds Orion and Paessler PRTG platforms with a unified MCP server, enabling querying, alert management, and governed writes with audit logging.
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    MIT
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    Classifies agent errors and provides clear recovery actions (retry, escalate) with configurable categories and strategies, plus MCP tools for direct agent use.
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    MIT
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    Relays error logs from other MCP servers when an LLM call fails and the returned error message is unclear, helping the model handle errors more intelligently.
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    MIT
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    Analyzes websites like a senior performance engineer and produces prioritized Core Web Vitals recommendations. Provides 16 tools for detailed performance analysis, including Lighthouse, CrUX, WordPress and Cloudflare inspections.
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    Enables web performance analysis using Google PageSpeed Insights and Chrome UX Report APIs, providing compact metrics and diagnostics without local browser automation.
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    MIT
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    Enables comprehensive analysis of AWS Lambda performance including cold start tracking, memory utilization monitoring, cost analysis, and optimization recommendations. Provides real-time metrics, comparative analysis across multiple functions, and actionable insights to improve Lambda performance and reduce costs.
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    Allows tracking of parcel deliveries across various carriers using the 17track.net API, with automatic or manual carrier detection.
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    MIT
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    A comprehensive, AI-powered performance analysis and monitoring platform for OpenShift/Kubernetes clusters. This project provides Model Context Protocol (MCP) servers for analyzing etcd, network, and OVN-Kubernetes components with deep performance insights, automated root cause analysis, and actionable recommendations.
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    Apache 2.0