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"How to understand a Google Sheet" matching MCP servers:

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    Enables AI assistants to understand and navigate codebases through structural analysis. Provides code mapping, symbol search, and impact analysis using ast-grep for accurate parsing of Python, JavaScript, TypeScript, and Go projects.
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    52
    MIT
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    MCP server that helps AI coding agents understand a repository by providing lightweight tree/map, code search, and token-budgeted context packing tools without dumping the entire monorepo into the prompt.
    3
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    MIT
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    Enables AI-assisted X++ development for Dynamics 365 Finance and Operations by pre-indexing the entire codebase and providing 54 specialized tools for metadata lookup, code generation, and best practice validation.
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    MIT
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    Analyzes codebases to generate dependency graphs and architectural insights across multiple programming languages, helping developers understand code structure and validate against architectural rules.
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    MIT
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    Enables AI coding assistants to understand codebase architecture in real time by parsing source code into a relationship graph and exposing call chains, dependencies, class hierarchies, and conventions via MCP tools.
    14
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    MIT
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    Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.
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    3
    MIT
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    Selects the minimum code context an LLM needs to understand a git diff: graph-based fragment selection under a token budget, deterministic output, 30+ tree-sitter languages.
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    Apache 2.0
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    A lightweight MCP server that provides 40 tools for TypeScript/JavaScript refactoring and code intelligence, directly mapping to TypeScript's tsserver protocol commands for accurate structural changes and workspace analysis.
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    MIT
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    A powerful tool designed to help, primarily LLMs, understand and navigate complex codebases. It functions both as a command-line application for on-demand analysis and as an MCP (Model Context Protocol) server, providing continuous repository mapping capabilities to other applications. By generating
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    MIT
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    Generates progressive-disclosure code context documentation (L0/L1/L2/L3) for large projects, helping AI coding agents understand codebases efficiently.
    3
    MIT
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    Blindspot is a local, general-purpose context engine for AI coding agents. It helps agents understand project structure, symbols, relationships, and change impact before editing.
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    MIT
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    quality
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    Enables AI coding agents to intelligently index and search codebases with sub-20ms retrieval, 8x memory compression, and cross-encoder reranking via MCP stdio.
    5
    MIT
  • F
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    A TypeScript tool that ranks files in your codebase by importance, tracks dependencies, and provides file summaries to help understand code structure through Cursor's Model Context Protocol.
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    Provides comprehensive codebase analysis and semantic understanding through integrated knowledge graphs, enabling AI assistants to understand project structure, patterns, dependencies, and context through multiple analysis tools and format generators.
    9