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    Enables natural language interaction with Plausible Analytics data to query traffic, visitors, engagement, and more using conversational questions.
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    MIT
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    Enables LLMs to query telemetry data via the Spyglass AI agent, providing intelligent insights about application performance, errors, and bottlenecks.
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    MIT
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    The AgentOps MCP server provides access to observability and tracing data for debugging complex AI agent runs. This adds crucial context about where the AI agent succeeds or fails.
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    MIT
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    A Model Context Protocol server that provides AI agents with controlled read access to Datalust Seq instances for log analysis and monitoring. It enables agents to search events, execute data queries, and retrieve information about signals, dashboards, and alerts.
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    MIT
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    MCP server for Umami Analytics that provides read-only tools to query website stats, events, sessions, reports, and more, enabling natural language analytics queries.
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    MIT
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    Self-hosted web analytics platform that exposes analytics data via the Model Context Protocol, enabling natural language queries about traffic, page views, web vitals, and custom events.
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    MIT
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    Provides Claude with access to Shodan for IP address lookups, service discovery, and vulnerability scanning, allowing users to query information about hosts, count results, and check API usage.
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    MIT
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    Provides read-only access to query and retrieve information about devices, fleets, events, and configurations managed by Flight Control through a safe integration layer supporting filtering and selector-based queries.
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    Connects AI assistants to turbometrics.de for website performance analysis, scans, real user monitoring, and alerts management.
    MIT
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    Exposes Umami analytics (Cloud or self-hosted) as read-only tools for querying website stats, pageviews, metrics, and active visitors.
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    MIT
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    Enables integration with Datadog APIs to monitor and retrieve information about monitors, metrics, dashboards, logs, events, and incidents through the Model Context Protocol.
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    MIT
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    Enables AI assistants to query and fetch error and performance monitoring data from AppSignal through the Model Context Protocol. Supports searching and retrieving detailed information about application errors and performance samples with flexible filtering options.
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    MIT
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    Enables comprehensive system monitoring and diagnostics through 18 tools that provide detailed information about CPU, memory, disk usage, network interfaces, running processes, battery status, hardware details, and temperature monitoring. Allows users to query system information and performance metrics through natural language interactions.
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    ISC