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    coldstart gives coding agents durable memory of a specific codebase, with no embeddings, no vector store, and no API key. Agents write notes after real tasks — what a file is for, how a flow spans files, which invariants hold — and those notes are surfaced automatically to a later session whose task matches, so nothing gets rediscovered from scratch. Every note is anchored to real files by content
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    36 npm
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    MIT
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    Call graph MCP server for Rust, Python, TypeScript, TSX, and JavaScript. Every caller and callee carries a resolution field showing how the reference was matched to a definition, plus multi-hop impact analysis for refactoring. Local-only, single binary, no network egress. Benchmarked against grep, ripgrep, and ctags on a 120-item hand-verified golden set, including the cases where it loses.
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    MIT
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    Enables AI agents to browse, read, search, blame, and diff files from any branch, tag, or commit in a self-managed GitLab instance without checking anything out or modifying the working tree. Content is served read-only from locally cached git mirrors or your own clones, so no GitLab REST API access is needed.
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    315 npm
    MIT
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    Code context for AI coding agents. Progressive, on-demand access to your internal .NET / NuGet package source — agents browse, search, and read private C# libraries autonomously, with zero workspace pollution.
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    22 npm
    MIT
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    Enables AI agents to navigate JS/TS, HTML, and CSS code — symbol info, outlines, callers, implementations, definitions, references, hover, and diagnostics — through bundled Node language servers. It also builds a cross-file index answering where CSS custom properties and #id/.class selectors are defined and used across stylesheets, markup, and scripts.
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    11 npm
    MIT
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    Enables AI assistants to access and interact with Cursor/VS Code Local History data for file recovery and enhanced context awareness. Provides tools to browse file history, search across snapshots, and restore previous versions of files.
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    Enables an MCP-capable assistant to review GitLab merge requests, GitHub pull requests, and pre-push local branches against ticket requirements, domain rubrics, compiler/lint pre-passes, blast-radius analysis, and SonarQube findings, and to draft inline comments or follow-up tasks in the user's chosen language. Nothing reaches a remote host until a human approves the exact content in an interactive terminal.
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    MIT
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    Enables AI agents to formally verify constant-time, masking, and patch completeness properties of Verilog hardware designs, providing concrete leakage signals and next-step guidance.
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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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    Lets AI coding agents read an outline of a large file or a digest of noisy command output instead of the full text, while every hidden line stays retrievable on disk through a run handle. Agents can request only the line ranges, slices, or grep matches they need and list recent runs, cutting token spend without losing information.
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    MIT
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    Enables AI coding agents to interact with TypeScript projects through compiler-level code intelligence, providing tools for navigation, type information, diagnostics, refactoring, and semantic search.
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    722 npm
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    Apache 2.0
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    An MCP server that enables AI agents to move TypeScript files and directories while automatically updating all affected imports using a persistent tsserver instance. This ensures atomic, error-free refactoring that maintains project integrity without manual intervention or wasted tokens.
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    16 npm
    MIT
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    An MCP server leveraging the Rust-based ty type checker to provide AI models with high-performance, semantic Python code analysis and structural navigation. It enables precise symbol searching, cross-file renaming, and diagnostic reporting to improve code understanding and editing accuracy.
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    MIT
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    Enables full-stack compiler development across the hw-native-sys workspace by combining repository operations (git health, code search, task running) with a knowledge layer for architecture docs, task routing, and abstraction/pass pipeline information.
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