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    Basic Memory is a knowledge management system that allows you to build a persistent semantic graph from conversations with AI assistants. All knowledge is stored in standard Markdown files on your computer, giving you full control and ownership of your data. Integrates directly with Obsidan.md
    17
    4,032
    AGPL 3.0
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    Provides a suite of deterministic tools for time calculations, math, and string manipulation that LLMs often struggle to perform accurately. It also includes utilities for secure randomness, data validation, and basic network operations like DNS lookups.
    5 npm
    MIT
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    A simple MCP server built with FastMCP for experimentation and learning purposes. Includes basic web tools like article fetching and serves as a human-readable template for building custom MCP servers.
    AGPL 3.0
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    Enables task management with persistent SQLite storage, along with tools, resources, subscriptions, prompts, and completions over stdio or Streamable HTTP with per-user authentication.
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    MIT
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    A minimal MCP server for text processing, providing word count, uppercase conversion, and a summarization prompt template.
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    A meta-server that aggregates multiple MCP servers into a single interface, reducing token usage by 98%+ through progressive tool discovery and direct code execution that processes data between tools without consuming context window space.
    16 npm
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    Apache 2.0
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    A different approach from typical persistent-memory MCPs. Instead of a local SQLite + embeddings store, the memory lives as plain files in a .ai-memory/ directory you commit to your repo (facts.jsonl, decisions/\*.md, gotchas.md). Git is the sync layer — what one Claude/Cursor/Cline learns about a repo, the next session (or a teammate's agent) picks up automatically. 5 MCP tools: get_rep
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    27 PyPI
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    Chain of Draft Server is a powerful AI-driven tool that helps developers make better decisions through systematic, iterative refinement of thoughts and designs. It integrates seamlessly with popular AI agents and provides a structured approach to reasoning, API design, architecture decisions, code r
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    MIT
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    Failure Memory provides AI coding agents with a shared local memory of failures, enabling them to record, recall, and learn from mistakes across sessions.
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    MIT
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    Enables AI assistants and developers to analyze code for language-specific best practices and idiomatic patterns across programming languages, CI automation, and configuration formats.
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    A multi-agent orchestration system that enables multiple Claude instances to collaborate through a centralized hub with a shared workspace and real-time communication. It features integrated task management, role assignment, and persistent memory to facilitate complex, synchronized agent workflows.
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    MIT