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"A server for centralized logging and log management" matching MCP servers:

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    An MCP server that lets AI coding agents save immutable, versioned Markdown reports into md-log, a human-in-the-loop review and archive layer for vibe coding.
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    MIT
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    Reduces log files to remove noise and duplicate information, cutting tokens by 70-90% for AI agents, preserving only errors, warnings, and unique events.
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    MIT
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    MCP server for collecting and analyzing CLI/web server error logs. Enables watching log files/directories, parsing common error patterns, and querying/analyzing logs through natural language.
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    MIT
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    Enables AI assistants to query, search, and analyze logs across Google Cloud Platform projects. It supports advanced filtering by severity or resource type and provides detailed log entry retrieval and project listing capabilities.
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    Enables MCP-compatible coding agents to debug applications using runtime log data, by providing tools to start a local log-ingestion server, track debugging sessions and hypotheses, and correlate logs to specific executions.
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    MIT
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    An MCP server that provides AI assistants with direct access to application logs for on-demand searching, filtering, and analysis. It enables tools like Cursor to summarize log entries and identify errors within the development environment to streamline debugging.
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    MIT
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    Acts as a persistent log of ad creative reviews. Stores each review's ad name, funnel stage, score, media link, and full review text so agents can look back at how past creatives were scored.
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    An MCP server that gives AI assistants tools to analyze Salesforce Apex debug logs for performance bottlenecks, slow methods, and governor limit usage.
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    BSD 3-Clause
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    Integrates Azure Cost Management APIs via MCP, enabling cost queries, forecasts, budgets, and Advisor recommendations through natural language in compatible clients.
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    Manage Job using MCP: Manage Job, Candidates, Resumes, Salaries all within this one MCP tools It can solve problems like: You have 50 resumes to screen. Your AI assistant can reason about candidates, but it can't: Read PDFs/DOCX — The AI can't open binary files Extract structured data — Copy-pasting loses formatting, metrics, and context Compare at scale — No consistent scoring across candida
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    MIT
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    An MCP server for unified cost tracking and analysis across AWS, OpenAI, and Anthropic. It enables users to query expenditures, compare costs across providers, and analyze usage trends through natural language.
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    MIT
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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