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"How to scrape data from websites into JSON" matching MCP servers:

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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.
    5
    16
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    MIT
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    Enables querying and formatting Loki logs from Grafana via the Model Context Protocol. It supports LogQL queries, label retrieval, and provides results in text, JSON, or markdown formats.
    29
    MIT
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    Query your Surfa Analytics data using natural language through Claude Desktop, ChatGPT, or any MCP-compatible client. Turn your analytics into insights with AI.
    3
    MIT
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    Multi-site analytics for AI agents — GA4, Google Search Console, Cloudflare edge, PageSpeed, and audit insights across all your websites as tools.
    21
    MIT
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    This remote MCP server allows interaction with the Dynatrace observability platform. Bring real-time observability data directly into your development workflow.
    MIT
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    Securely integrates Wazuh security data with LLMs (such as Claude Desktop) by retrieving alerts from Elasticsearch indices and transforming them into MCP-compliant format, enabling real-time security context in LLM applications.
    217
    MIT
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    Aggregates site analytics and published content from Google Analytics, Search Console, Cloudflare, and Buffer into MCP tools for traffic, search, request, and content reports.
    Apache 2.0
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    MCP server for Prometheus metrics and observability. Give Claude (or any MCP-capable agent) read access to your Prometheus instance — query metrics with PromQL, inspect active alerts, and explore scrape targets without leaving the conversation. Tools: prometheus_list_metrics, prometheus_query, prometheus_query_range, prometheus_list_alerts, prometheus_list_targets.
    20
    MIT
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    Exposes live industrial IoT telemetry to any MCP client, streaming simulated sensor data from a fleet of machines and detecting anomalies, with the ability to inject faults on demand.
    4
    MIT
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    An MCP server that decodes V8 CPU profiles into token-efficient bottleneck summaries for AI agents, enabling them to analyze CPU consumption, call trees, GC pressure, and async bottlenecks.
    6
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    MIT
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    An on-call agent for data incidents, built on a bio-inspired context protocol. It triages data quality and freshness issues using DataHub, computing severity, blast radius, and ownership from the catalogue before generating prose.
    9
    Apache 2.0