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    MCP server for on-premises Azure DevOps that lets AI assistants browse repositories, review pull requests, manage work items, and interact with wikis, with NTLM authentication support.
    35
    Apache 2.0
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    Sandboxed Python execution with automatic dependency management. Executes Python scripts in isolated environments (bubblewrap or Docker) with PEP 723 inline dependencies, preventing host pollution.
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    MIT
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    Enables LLM-driven agents to autonomously detect, diagnose, repair, verify, and prevent software and hardware failures on local and remote systems. Includes built-in safety checks and automatic rollbacks.
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    An MCP server for read-only Linux system administration and diagnostics on RHEL-based systems via SSH. It enables users to troubleshoot remote hosts by accessing system information, services, logs, and network configurations through natural language.
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    Apache 2.0
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    PhysBound is a specialized "Physics Linter" for AI that deterministically validates RF and thermodynamic claims against hard physical limits, preventing hallucinations in engineering workflows.
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    MIT
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    A personal memory engine and MCP server that stores durable facts in markdown files managed via git, enabling hybrid search (lexical + semantic) through an MCP interface for persistent context across LLM sessions.
    3
    MIT
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    Code-mode MCP server (docs_search + execute_code two-tool surface) backed by a unified capability manifest across three sandbox kernels (in-process node:vm, WASM via QuickJS / Pyodide / Wasmtime, and remote microVM via E2B / Cloudflare Sandbox). At N=30 tools the bootstrap-context cost drops to 13.6% of direct tool-use. Apache-2.0.
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    Apache 2.0
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    Enables AI assistants to render 3D models from OpenSCAD code, generating single views or multiple perspectives with full camera control. Supports animations, custom parameters, and returns base64-encoded PNG images for seamless integration.
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    MIT
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    Enables AI assistants to interact with Intervals.icu fitness tracking and wellness data, allowing users to fetch, filter, and group activities or health metrics. It provides structured summaries of workouts and physical well-being through natural language queries.
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    Enables management of Kylas CRM lead operations, including creating leads, searching and filtering records, and resolving user, product, or pipeline IDs. It provides specialized tools for monitoring idle leads and accessing lead schema instructions through natural language.
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    MIT
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    Enables AI assistants to query KumoRFM for predictive analytics on relational data, including graph management, natural language to PQL conversion, and training-free predictions.
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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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    MIT
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    MCP server for the Trading 212 public API, enabling account, portfolio, order, pie, and history access with read tools always available and trading tools opt-in.
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    MIT
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    Provides CodeQL language intelligence to AI agents via MCP, enabling completions, hover, definitions, references, diagnostics, formatting, and in-memory file updates for QL code.
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    MIT