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    Enables AI assistants to search, analyze, and debug application API traffic captured by Tusk Drift, including HTTP requests, database queries, distributed traces, latency metrics, and error rates.
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    MIT
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    Enables inspection and management of Apache Airflow DAGs, runs, and logs across multiple instances. It provides tools for monitoring workflows and performing gated write operations like triggering DAGs or clearing task instances.
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    16
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    Apache 2.0
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    A read-only MCP server for AppDynamics that exposes 12 task-oriented tools to query applications, topology, metrics, snapshots, health rules, anomalies, events, alerting config, analytics events, and dependency maps via OAuth API-client credentials.
    Last updated
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    MIT
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    Enables real-time tracking of AI task starts and completions with integrated webhook notifications for monitoring activity. It allows users to log progress and send task-related event data to external services via configurable webhook URLs.
    Last updated
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    MIT
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    Lets your coding agent talk to the RunWhen platform — workspace chat, issues, SLXs, run sessions, and the Tool Builder — over the Model Context Protocol. Enables workspace chat with AI assistant, task authoring via Tool Builder, and direct data access to workspaces, issues, SLXs, run sessions, and more.
    Last updated
    45
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    Apache 2.0
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    Monitor and manage Apache Airflow clusters through natural language queries via MCP tools: DAG inspection, task monitoring, health checks, and cluster analytics without API complexity. * Guide: https://call518.medium.com/mcp-airflow-api-a-model-context-protocol-mcp-server-for-apache-airflow-5dfdfb2
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    MIT
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    An MCP server that enables AI assistants to manage, monitor, and diagnose Windows systems through 42 tools across 8 modules, including services, event viewer, task scheduler, processes, network, diagnostics, observability, and safety features.
    Last updated
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    MIT
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    Password-protected dashboard and Remote MCP observer for Codex work, recording concise task milestones via observe_start, observe_report, and observe_finish tools.
    Last updated