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    A remote MCP server that provides AI agents access to the Rootly API for incident management, allowing users to query and manage incidents, alerts, teams, services, and other incident management resources through natural language.
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    Enables querying Margint per-customer AI margins and costs from MCP clients. Provides tools to list customers, drill into costs, check budgets, and view workspace-wide cost breakdowns.
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    MIT
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    ac-infinity-mcp is an MCP server that connects Claude (and other AI assistants) directly to your AC Infinity controllers — so you can read live sensor data, run analytics, and adjust fan speeds or port states using natural language, without opening the AC Infinity app.
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    MIT
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    Exposes a Kubernetes cluster to MCP-compatible AI clients, enabling read-only and optional write operations on cluster resources like pods, deployments, and namespaces through natural language.
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    9
    MIT
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    MCP server for Drumbeats monitoring. Enables creating monitors, triaging incidents, and running HTTP/SSL/DNS checks using natural language from any AI client.
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    Apache 2.0
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    Enables managing and debugging Azure Logic Apps through natural language, with tools for listing, debugging, creating, updating, and deleting workflows.
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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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    MCP server for the beacon log analytics platform, exposing beacon's REST API as MCP tools and resources for AI agents to query and analyse AI-assistant usage data.
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    11
    MIT
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    Bridges AI agents to the Lenx.ai social media monitoring API, enabling task management like listing, creating, and exporting monitoring data.
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    MIT
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    Provides advanced, programmable access to Zabbix monitoring data and management features, enabling querying, automation, and integration of Zabbix resources.
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    MIT
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    Estimates the environmental footprint of your AI use — energy (kWh), miles driven, water used for cooling, and CO₂ — plus a prompt-efficiency score, working with any AI client by measuring token usage.
    Last updated
    9