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    A personal AI coding assistant that connects to various development environments and helps automate tasks, provide codebase insights, and improve coding decisions by leveraging the Model Context Protocol.
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    Integrates Google's Gemini AI with Claude Code to provide advanced development assistance including code analysis, implementation planning, technical explanations, debugging support, and strategic development guidance.
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    MIT
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    Integrates GitHub Copilot with MCP-compatible tools to provide AI-powered code assistance, including chat, code explanation, and reviews. It leverages existing GitHub CLI authentication to support multiple models like GPT-4o and Claude 3.5 Sonnet.
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    Wraps the cursor-agent CLI to provide cost-effective tools for repository analysis, code search, planning, and editing. Offloads heavy thinking tasks from the host AI to reduce token usage while maintaining precise, scoped workspace operations.
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    An MCP server that enables Claude and other LLM clients to access Hanzo AI platform capabilities and provide powerful code management tools for analyzing, modifying, and understanding projects.
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    Combines AST intelligence and guarded file operations to provide a secure, controlled repository workflow for coding agents, enabling structural code analysis and safe edits without unbounded editor access.
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    MIT
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    Local-first MCP server that indexes all projects on your machine and provides coding agents with briefings about each project's stack, services, and run instructions, so they can start sessions with context across all repos. No cloud, no accounts, no telemetry.
    962
    MIT
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    A local-first memory layer for coding agents to persist and retrieve project decisions, architecture context, and rules across multiple development sessions. It utilizes a three-tier memory model and hybrid retrieval to provide agents with durable, searchable context and a WebUI for human review.
    4
    MIT
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    Indexes local Python code into a Neo4j graph database to provide AI assistants with deep code understanding and relationship analysis. Enables querying code structure, dependencies, and impact analysis through natural language interactions.
    4,099
    MIT
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    MCP server that automates the generation of production-grade async FastAPI services following Clean Architecture, with self-healing pytest-based verification and OpenAPI spec ingestion for rapid CRUD API development.
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    A code understanding and generation system designed for Galacean Engine that helps LLMs comprehend the engine architecture and generate high-quality engine code based on user intent.
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    Official implementation of Anthropic's 'think' tool that provides Claude with a dedicated space for structured reasoning, improving performance by up to 54% on complex tasks requiring multi-step problem solving.
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    MIT