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Tools for slow thinking, step-back reasoning, and contextual memory capabilities

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  • Why this server?

    This server implements human-like cognitive architecture, featuring 'dual-process thinking' (intuitive and deliberative reasoning modes), which directly addresses the concept of '慢思考' (slow thinking).

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    Implements human-like cognitive architecture for enhanced AI reasoning through dual-process thinking, memory systems, emotional processing, and metacognitive monitoring. Enables users to process thoughts with biological-like cognitive processes including intuitive and deliberative reasoning modes.
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    208
    1
    MIT

  • Why this server?

    This system provides 'structured, iterative reasoning' and 'revision mechanisms,' perfectly matching the requirements for both '慢思考' (slow thinking) and '退一步思考' (step-back thinking).

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    Enables structured, iterative reasoning for complex problem-solving with features like confidence tracking, revision mechanisms, and branching support. Provides flexible validation and multiple output formats for systematic analysis and decision-making tasks.
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    1
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    MIT

  • Why this server?

    This server implements an enhanced sequential thinking tool with 'self-auditing thought steps' and 'branching support,' which facilitates reflective analysis ('退一步思考') for complex coding problems.

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    An enhanced sequential thinking tool optimized for programming tasks that helps break down complex coding problems into structured, self-auditing thought steps with branching and revision capabilities.
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    MIT

  • Why this server?

    Specialized in metacognitive monitoring, this server detects reasoning loops and provides intelligent recovery, directly supporting '退一步思考' (thinking back a step) to refine solutions.

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    A Model Context Protocol server that empowers AI agents with metacognitive monitoring to detect reasoning loops and provide intelligent recovery using case-based reasoning and statistical analysis.
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    MIT

  • Why this server?

    A lightweight short-term memory server explicitly designed to 'automatically stores and recalls working context, session state, and task progress,' fulfilling the '上下文记忆能力' (contextual memory capability).

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    A lightweight short-term memory MCP server that automatically stores and recalls working context, session state, and task progress for AI agents. Memories auto-expire after 24 hours and integrate seamlessly with workspace-aware storage across multiple projects.
    Last updated
    10
    MIT

  • Why this server?

    Enables robust '上下文记忆能力' (contextual memory) by storing conversational context and domain knowledge in a persistent graph structure that survives across multiple sessions.

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    A persistent memory implementation using a local knowledge graph that lets Claude remember information about users across conversations.
    Last updated
    9
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    MIT

  • Why this server?

    Focuses on providing time intelligence, 'persistent memory' and 'complete traceability' for AI agents, ensuring high-quality '上下文记忆能力' (contextual memory).

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    Provides time intelligence, persistent memory and complete traceability for AI coding agents. Enables sophisticated activity tracking, timezone management, smart reminders, and cross-session continuity with support for both per-project and centralized storage modes.
    Last updated
    9
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    MIT

  • Why this server?

    Provides a framework for 'structured, iterative reasoning' to break down complex tasks, which is central to '慢思考' (slow thinking) and controlled problem-solving.

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    Provides a tool for dynamic and reflective problem-solving by breaking complex problems into manageable steps with support for revision, branching, and hypothesis generation.
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    MIT

  • Why this server?

    Enables AI assistants to 'reflect on, critique, and continuously improve their performance,' providing a strong mechanism for '退一步思考' (reflective analysis).

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    Enables reflective thinking and memory storage with support for multiple AI providers (OpenAI, Anthropic, Gemini, Ollama) or local fallback. Stores contextual memories locally and provides tools for reflection, questioning, note-taking, and summarization across different conversation keys.
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