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    An MCP server for managing work logs, research results, and task checkpoints to enable seamless collaboration and state recovery between AI agents. It provides a persistent memory layer for tracking project history and resuming workflows across different sessions or tools.
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    Transforms HTML into Figma designs using only the user’s design system, ensuring correct component usage or safe fallback to primitives, while continuously learning from each build to improve mapping accuracy and consistency.
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    MIT
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    Lightweight MCP server that exposes tools for system information and weather lookup, designed for agent integration via stdio.
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    Connects MCP clients directly to a live Godot editor, enabling AI assistants to build scenes, edit nodes, and control the editor through over 120 operations.
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    1,152
    MIT
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    A read-only MCP server that provides document awareness for agents by parsing local files into structured profiles, blocks, chunks, and search results, enabling agents to understand and cite document content without dealing with raw file formats.
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    Apache 2.0
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    A productivity coaching assistant designed for ADHD support that provides intelligent task management, goal tracking, and personalized recommendations. It utilizes a persistent memory system to learn user patterns and preferences, helping to reduce decision paralysis through actionable suggestions.
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    Personal multi-LLM memory repository using Markdown as source of truth, SQLite FTS5 for retrieval, and MCP tools for search, context, and write proposals.
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    Apache 2.0
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    An MCP server that enables AI assistants to control Blender through 108 specialized tools for 3D modeling, animation, and rendering. It provides a secure, thread-safe interface to execute validated operations in Blender using natural language commands.
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    AGPL 3.0
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    MCP server for Pokémon battle advising: provides type matchups, damage calculation, party analysis, and move/ability data for AI agents like Claude Code and Codex.
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    MIT
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    Carbon-aware MCP scheduler that defers "do it later" / "by tomorrow" / "overnight" LLM tasks to the cleanest electricity-grid hour inside a deadline — 40-70% lower CO2 per task, 50% cheaper via Anthropic/OpenAI Batch APIs. 9 MCP tools, per-task carbon receipts to local SQLite, Apache-2.0.
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    Apache 2.0
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    MCP server for secure, session-based Python code execution in Docker containers, enabling LLM applications to run code, manage state, and access files.
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    MIT
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    Full computer control for AI agents on macOS — see, click, type, scroll via MCP. Works with any app, all processing on-device with PII protection.
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    MIT