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"Methods to Extract Text Content from Videos" matching MCP servers:

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    Enables automated migration of Vue projects to React with intelligent analysis, task management, and learning from user modifications.
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    MIT
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    Enables AI coding agents to extract Figma design data and convert it into Flutter widgets and screens. Supports theme setup, component analysis, asset exports, and provides Flutter implementation guidance from Figma designs.
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    MIT
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    Enables coding agents to perform safe, project-wide Python refactoring (rename, move, extract, inline, change signature, organize imports, etc.) with a dry-run safety contract and LSP-coordinate addressing.
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    MIT
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    Exposes tools for AI assistants to query a persistent SQLite+FTS5 index of C/C++ symbols parsed from real build commands, enabling sub-millisecond lookup, full-text search, and natural-language explanation without hallucination.
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    MIT
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    Extract domain knowledge from codebases to reduce LLM token consumption by 20x and time in agentic search by 10x — gathers and makes concepts, naming conventions, and vocabulary queryable via MCP.
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    MIT
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    Provides Python refactoring capabilities via the Rope library, enabling AI agents to perform safe, project-wide code transformations such as renaming symbols, moving modules, and extracting methods.
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    MIT
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    MCP server integrating Rizin reverse engineering, RzGhidra decompiler, and capa for binary analysis, enabling LLMs to open binaries, identify capabilities, extract function addresses, and decompile to C pseudocode.
    10
    MIT
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    An MCP server that scans codebases to extract structural information (classes, functions, etc.) with flexible filtering options and outputs in LLM-friendly formats.
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    GPL 3.0
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    Analyzes codebases and extracts all symbols (functions, classes, methods, interfaces, etc.) from 10+ programming languages into LLM-optimized markdown format. Enables AI assistants to understand entire project structures efficiently without processing full source code.
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    MIT
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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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    MIT
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    AI-powered characterization test generator that reads Python functions or class methods, synthesizes inputs, captures behavior in a sandbox, and emits pytest files to lock legacy code behavior for safe refactoring.
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    Apache 2.0