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    A comprehensive MCP server that provides AI assistants with tools for file system management, Git integration, and shell command execution. It features specialized code utilities for analysis, formatting, and linting to enhance development workflows within Claude Desktop.
    28
    7
    MIT
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    An MCP server for SonarQube that enables LLM agents to discover projects, analyze code quality metrics, check Quality Gate status, search issues with filters, and rank projects by worst-performing metrics. It provides read-only, safe access to SonarQube instances with structured outputs and error handling.
    5
    MIT
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    A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.
    3
    3
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    An MCP server that provides local code quality analysis for AI coding assistants, supporting file analysis, git diff review, and full project scanning with quality scoring.
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    MIT
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    MCP server for E2B code sandbox, enabling creation, management, and execution of code in secure sandboxes using Python and shell commands.
    4
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    MIT
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    Enables managing audit findings in a SQLite database via natural language, allowing queries and updates without writing SQL.
    3
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    Strict AI code reviewer powered by Groq. Finds bugs, vulnerabilities and security issues in your code. Supports analyze, compare versions, explain code, generate tests, and HTML reports.
    7
    2
    MIT
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    Intelligent code indexing and analysis for Large Language Models, enabling advanced search, file analysis, and real-time monitoring of codebases.
    12
    MIT
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    Helps AI assistants optimize Linux workloads on Arm64 by parsing perf report output, recommending NEON SIMD intrinsics for hot loops, and auditing Python dependency manifests for arm64 wheel availability — all offline and structured.
    4
    Apache 2.0
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    Gives coding agents a memory of codebases by searching repositories using semantic similarity and structural call/import graphs, enabling reuse of proven patterns and reducing token usage.
    6
    1