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"How to get the file tree structure of a folder" matching MCP servers:

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    Analyzes source code structure across multiple languages using tree-sitter, extracting classes, functions, methods, and metadata with precise line numbers for efficient codebase exploration and AI-assisted development.
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    3
    MIT
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    Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.
    3
    27
    Apache 2.0
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    Enables AI assistants and developers to analyze code for language-specific best practices and idiomatic patterns across programming languages, CI automation, and configuration formats.
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    MIT
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    Enables AI applications to write and execute natural language queries on organizational data stored in DX Data Cloud's Postgres database. Runs locally and provides a query tool for AI assistants to formulate and execute database queries with user approval.
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    MIT
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    Enables human-in-the-loop interaction for AI coding workflows through native desktop dialogs that present route choices, text input, and image annotation capabilities when AI assistants encounter decision points.
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    MIT
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    Bridges a local AI agent to a Colab session in the browser, enabling code execution and file operations through the Model Context Protocol.
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    Apache 2.0
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    A multi-domain MCP server that exposes a small, stable surface of three tools (list_domains, list_domain_tools, call_domain_tool) to gate access to various tool domains like Plane and BloodBank, preventing agent schema overload.
    3
    MIT
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    Enables comprehensive threat analysis for Indicators of Compromise (IoCs) including IP addresses, file hashes, domains, and URLs. It provides detailed reputation scores, security vendor evaluations, and network metadata to facilitate security assessments and risk detection.
    4
    MIT
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    A template and demonstration project for building, testing, and deploying remote MCP servers using FastMCP and uv. It provides a foundational structure for creating MCP-compliant tools that can be hosted publicly and integrated with LLM agents.
    2
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    Local-first MCP server for controlling Google Colab as a development, shell, file, and training runtime, with tools for notebook editing, GPU acceleration, and file transfer.
    59
    7
    Apache 2.0
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    Read-only MCP server for the Bucharest Stock Exchange, exposing instrument data, OHLCV candles, and fundamentals through a public backend with no authentication.
    9
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    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 query, analyze, and discover FileMaker database schemas via OData v4, with pandas-powered analytics for efficient data summaries.
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    GPL 3.0
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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.
    4