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"A tool or method to edit coding projects and manipulate files" matching MCP servers:

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    Architectural Gatekeeper for AI coding. Prevents "tunnel vision" bugs by forcing the AI to verify dependencies (via AST parsing) before editing files. Supports JavaScript & TypeScript. Blocks unsafe edits until the AI proves it understands the impact
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    Apache 2.0
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    Provides intelligent semantic code search using local AI embeddings, enabling natural language queries to find relevant code by meaning rather than exact keywords. Indexes codebases in the background with smart project detection and privacy-first local processing.
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    MIT
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    Enables agents to quickly find and edit code in a codebase with surgical precision. Find symbols, edit them everywhere with tools for reading code blocks, searching/replacing text, and making precise line-based modifications.
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    Enables AI assistants to perform intelligent semantic code search across codebases using local AI embeddings for meaning-based retrieval.
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    MIT
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    Provides AI assistants with comprehensive Magento 2 coding standards, security rules, and theme-specific guidelines to ensure generated code is compliant. It enables real-time code validation, pattern lookup, and security auditing tailored to different Magento frontend stacks.
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    MIT
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    Enables AI assistants to leverage Qwen's code analysis capabilities with large context windows, supporting file/directory analysis, sandbox execution, and multiple approval modes for safe code operations.
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    MIT
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    Integrates the Atlassian Rovo Dev CLI with the Model Context Protocol, allowing AI assistants to perform deep code analysis using Rovo Dev's large context capabilities. It enables features like repository-wide queries, file-specific analysis, and specialized coding modes through a standard MCP interface.
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    MIT
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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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    CodeGuard MCP is a real-time AI code security scanning tool used to detect vulnerabilities, keys, and compliance issues in AI-generated code, and is suitable for code security reviews in development environments
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    Apache 2.0