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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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    A read-only MCP server that exposes local coding-agent session logs as three tools for introspection of recent work, debugging tool failures, and tracking token usage and estimated cost without parsing log files.
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    3
    MIT
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    An MCP server for AI-powered log analysis that enables parsing, searching, and debugging across nine log formats directly within Claude. It features automated error extraction, sensitive data scanning, and streaming support for large log files.
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    MIT
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    Local-first dashboard + MCP server that parses Claude Code and Codex JSONL files into a SQLite cost / token tracker. Per-MCP and per-tool breakdown, session drill-down, dedup by request_id; never talks to vendor APIs
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    MIT
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    iOS leak hunting and performance investigation. Reads .memgraph and .trace files, classifies retain cycles against a 34-pattern catalog with Swift fix templates, bridges to source via SourceKit-LSP. 28 MCP tools, 34 catalog resources, 5 investigation prompts.
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    Apache 2.0
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    Enables AI agents to analyze ROS2 log files across multiple nodes by merging them into a unified timeline and providing smart query tools, without requiring ROS2 installation.
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    MIT
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    Provides tools for AI assistants to manage and diagnose MWAA Airflow environments, EMR Serverless jobs, S3 files, and Confluence documentation. It features automated pipeline failure diagnosis and comprehensive log retrieval across these integrated platforms.
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    MCP server for log file analysis. Gives LLMs the ability to efficiently analyze large log files without loading them into context.
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    MIT
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    An MCP server that enables LLMs to analyze pcap files by providing tools for packet dissection, stream following, and data extraction via tshark. It supports protocol hierarchy analysis, credential scanning, and threat intelligence checks on captured network traffic.
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    MIT
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    Enables LLMs to load and analyze PCAP/PCAPNG files using Wireshark's sharkd interface, supporting packet inspection, traffic structure, conversations, and protocol statistics through natural language.
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    MIT
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    Enables AI-assisted analysis of log files through advanced searching, filtering, and test execution capabilities. Supports time-based queries, pattern matching, test summarization, and code coverage reporting directly within compatible MCP clients.
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    MCP server for analyzing OpenTelemetry traces with performance and error diagnosis, supporting loading from files, AWS X-Ray, and CloudWatch.
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    MIT
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    Enables LLMs to analyze logs by extracting patterns, redacting secrets, and providing token-efficient summaries from files, Docker containers, or journald.
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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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