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"Understanding Structured Thinking" matching MCP servers:

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    MCP server for Kibana / Elasticsearch — log search, aggregations, index discovery, and dashboard browsing. Hits Elasticsearch REST API directly for log queries; falls back to Kibana Console proxy when no direct ES URL is configured. Supports ApiKey auth (best for agents), Basic auth, and anonymous access. All 5 tools are read-only (readOnlyHint: true). Returns structured JSON (outputSchema).
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    MIT
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    A read-only kubectl MCP server for AI assistants. The kubectl verb is hardcoded in each tool (get/describe/logs/events) and user input only fills argument values — no mutation path even with malicious input, and Secret/ConfigMap values are never returned (metadata only). Returns structured JSON, plus one-shot k8s_triage (health scan) and k8s_inventory (cluster snapshot).
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    MIT
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    MCP server that transforms raw log query results into structured incident reports (Markdown, JSON, HTML) with correlation to git commits and tickets.
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    MIT
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    An MCP server for SonarQube that enables LLM agents to discover projects, analyze code quality metrics, check Quality Gate status, search issues with filters, and rank projects by worst-performing metrics. It provides read-only, safe access to SonarQube instances with structured outputs and error handling.
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    MIT
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    Enables LLMs to analyze network packet captures (PCAP files) from local or remote sources through a modular architecture. Supports DNS traffic analysis with structured JSON responses for network security and troubleshooting tasks.
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    MIT
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    Enables AI agents to query Windows process, window, and console information via structured JSON instead of screenshots, reducing token usage by 94-98%.
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    A safe, structured MCP server that lets AI inspect and operate a VPS through typed, allowlisted tools for nginx, PM2, SSL, UFW, fail2ban, with read-only defaults and opt-in mutations.
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    A FastMCP server that provides LLMs with structured access to Scalene's CPU, GPU, and memory profiling for Python applications. It enables automated performance analysis, bottleneck identification, and optimization suggestions through natural language interactions in supported IDEs.
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    Read-only PostgreSQL performance auditing via MCP. Collects metrics from PostgreSQL system views, applies deterministic checks for index, query, vacuum, configuration, and connection issues, and returns structured findings for MCP clients.
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    AI development observability platform. It silently captures structured data from vibe coding sessions via MCP and codifies deviation patterns into project rules to make AI write better code. 100% local and zero runtime dependencies.
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    MIT
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    Voice-powered bug reporting MCP server with 13 tools. Captures console logs, network errors, DOM traces, and developer voice descriptions. AI reads structured reports to find and fix bugs automatically. Chrome Extension + Python CLI.
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    MIT
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    Enables AI agents to profile iOS/macOS apps using Xcode Instruments, automate simulator interactions, and get structured performance data for optimization.
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    18
    MIT
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    A read-only MCP server that provides AI assistants with structured access to SonarQube projects, issues, metrics, and rules. It enables safe analysis of code quality and security findings through a set of validated, safety-first tools.
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    Enables querying and exploring Cribl Stream and Edge deployments, providing access to worker groups, fleets, sources, destinations, pipelines, routes, event breakers, and lookups through a structured interface.
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    MIT No Attribution
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    Integrates Charles Proxy with MCP clients to provide real-time and historical network traffic capture and structured analysis. It features a summary-first approach that filters noise and desensitizes data for efficient, low-token agent debugging and monitoring.
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    Enables access to Sentrul's research, compliance scanning, and trace lookup agents through MCP clients. Provides 257 production agents for analyzing web content, detecting compliance patterns, and retrieving structured trace data.
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    MIT
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    Enables LLMs to query and analyze logs from SEQ structured logging server with capabilities for searching events, retrieving event details, analyzing log patterns, and accessing saved searches.
    5
    MIT