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"Deploying an MCP service in an offline environment" matching MCP servers:

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    Enables local AI inference using Ollama and open-weight models, designed for offline operation in low-connectivity environments.
    6
    MIT
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    Enables AI agents to manage a Canonical Landscape estate, including inventory, alerts, patching, and script execution, with built-in safety layers to prevent accidental destructive actions.
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    MIT
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    Provides GDB and clangd-based tools for debugging C++ programs, enabling step execution, variable inspection, diagnostics, and combined runtime-static analysis.
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    MIT
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    Enables AI assistants like Claude to interact with humans through intuitive GUI dialogs, supporting text input, choices, confirmations, and information displays.
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    MIT
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    A persistent Python REPL MCP server for AI agents with stateful execution, real timeouts, crash isolation, and an MCP bridge to call other tools in the project.
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    MCP-сервер налоговых/бухгалтерских калькуляторов ФНС + публичные фискальные статусы (самозанятый, ИП/ОКВЭД, дисквалификация, блокировки счетов). Волна 1, юр-риск минимальный.
    12
    1
    MIT
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    A demo MCP server that returns an interactive dashboard (bar chart with dataset selector) as an MCP App via the official MCP Apps extension (SEP-1865).
    1
    MIT
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    An MCP server that enables AI assistants to search royalty-free stock photos using natural language, example images, or similarity, and render results as an inline thumbnail grid.
    3
    MIT
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    An MCP server that provides utility tools for text analysis, URL metadata fetching, and temperature conversion.
    3
    MIT
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    An MCP server that wraps the U.S. Army Corps of Engineers (USACE) Institute for Water Resources (IWR) River Mile Marker ArcGIS Feature Service, allowing LLMs to locate river mile markers on navigable U.S. rivers and answer 'what river mile am I at?' questions.
    3
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    Enables AI agents to interact with Freeplay, an ops platform for AI engineering teams, to analyze production logs, identify quality issues, iterate on prompts and agents using real data, and run experiments before deploying.
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    Apache 2.0