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    Enables multiple AI coding tools to collaboratively review code by sharing a SQLite-backed board, allowing them to create threads, post and reply to comments, list threads, and update statuses through MCP.
    16
    MIT
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    Provides A-share (Chinese stock market) quantitative analysis through tools for stock screening, northbound capital flow tracking, dragon-tiger list analysis, margin trading, sector analysis, technical indicators, IPO info, and limit-up/down statistics using akshare data.
    11
    2
    MIT
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    Renders source code as beautiful SVG or PNG images with syntax highlighting, line numbers, and diff support. Ideal for AI agents to present code visually to humans or for sharing snippets.
    2
    14 npm
    MIT
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    Wraps Claude Code as tools for MCP clients, enabling autonomous coding tasks via a 4-tool lifecycle with session management, async polling, and permission controls.
    4
    43 npm
    20
    MIT
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    Search and resolve MCP server packages on the mpak registry, enabling agents to discover servers, inspect tools, and check credential requirements before installation.
    2
    MIT
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    Knowledge graph for token-efficient code reviews. Builds a structural map of your codebase with Tree-sitter, tracks changes incrementally, and gives AI agents precise context via MCP tools. Features fixed multi-word search, qualified call resolution, dual-mode embedding (ONNX local + LiteLLM cloud), and output pagination.
    6
    76
    Apache 2.0
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    A production-grade MCP server for the Code-Fundi API, enabling AI agents to map codebases, search semantically, and analyze blast radius. It provides tools for repository management, AI-powered research, and impact analysis before shipping changes.
    27
    42 npm
    1
    MIT
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    A lightweight MCP server providing common utility tools such as unit conversion, password generation, date calculation, hashing, Base64 encoding/decoding, UUID generation, text case conversion, and text statistics, with no external dependencies.
    8
    MIT
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    Gives LLM agents spatial awareness inside Autodesk Maya, enabling scene inspection and measurement, aesthetic and engineering audits, camera planning, checkpoint/rollback safety, asset import, and viewport or render previews. Agents can also execute arbitrary Python in Maya to build and verify 3D scenes against engineering rules instead of writing blind.
    25
    222 PyPI
    2
    MIT
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    Enables natural-language interaction with Ladybug Tools for building performance simulation, 3D modeling, and data visualization, allowing agents to perform complex environmental analysis tasks without CAD software.
    3
    36 PyPI
    11
    GPL 3.0