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⇱ A server for managing systematic Drupal module development with advanced memory and project features | Glama


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A server for managing systematic Drupal module development with advanced memory and project features

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  • Why this server?

    This server provides persistent memory across sessions, storing up to 10,000 memories with semantic search and automatic context bridging. Its features like temporal versioning and anti-degradation protocols directly address the user's need for rich metadata, timestamping, TTL-based expiration, memory versioning, and context reconstruction across chat sessions for systematic development.

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    Provides Claude Desktop with persistent memory across sessions, storing up to 10,000 memories with semantic search and automatic context bridging. Features temporal versioning and anti-degradation protocols to maintain conversation continuity.
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  • Why this server?

    Described as an 'enhanced memory repository' that builds and queries 'temporally-aware knowledge graphs' from user interactions and data. Its support for 'task management' and broader AI model compatibility aligns with the user's project management features, organizational intelligence, and the need for linking/relationships within Drupal module development.

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    An enhanced MCP server that provides intelligent memory and task management for AI assistants, featuring semantic search, automatic task extraction, and knowledge graphs to help manage development workflows.
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    MIT
  • Why this server?

    This server offers a 'high-performance persistent memory system' with 'vector search', 'semantic knowledge storage', and 'efficient relationship management'. These capabilities directly support advanced tagging, full-text and semantic search across memory content, and memory linking/relationships critical for Drupal module development.

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    🧠 High-performance persistent memory system for Model Context Protocol (MCP) powered by libSQL. Features vector search, semantic knowledge storage, and efficient relationship management - perfect for AI agents and knowledge graph applications.
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    7
    MIT
  • Why this server?

    Provides 'knowledge graph-based persistent memory' for LLMs, allowing them to 'store, retrieve, and reason about information across multiple conversations and sessions'. This is a strong fit for core memory operations, memory types (project-level vs. chat-session), and context reconstruction.

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    A Model Context Protocol server that provides knowledge graph-based persistent memory for LLMs, allowing them to store, retrieve, and reason about information across multiple conversations and sessions.
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  • Why this server?

    This customized MCP memory server enables 'creation and management of a knowledge graph' with features like 'custom memory paths and timestamping'. This aligns with storing memories with rich metadata, automatic timestamping, and managing relationships for structured development.

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    A customized MCP memory server that enables creation and management of a knowledge graph with features like custom memory paths and timestamping for capturing interactions via language models.
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  • Why this server?

    Enhances the MCP memory server with 'robust document storage' and enables the 'creation and management of a knowledge graph that captures interactions via language models'. This supports metadata, relationships, and pattern detection across memories.

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    Enhances the MCP memory server by implementing PouchDB for robust document storage and enabling the creation and management of a knowledge graph that captures interactions via language models.
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  • Why this server?

    Creates and maintains a 'semantic knowledge graph of code' that allows 'maintaining context across sessions' and provides 'advanced search capabilities'. This directly addresses the need for organizing development knowledge, linking, and advanced querying for Drupal modules.

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    Creates and maintains a semantic knowledge graph of code that allows maintaining context across sessions with Claude, providing advanced search capabilities without requiring the entire codebase in the context window.
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  • Why this server?

    Enables clients to 'remember user information and preferences across conversations' using 'vector search technology'. This is useful for maintaining context, personalized experiences, and efficient retrieval of relevant memories.

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    An MCP server that enables clients like Cursor, Claude, and Windsurf to remember user information and preferences across conversations using vector search technology.
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    13
    MIT
  • Why this server?

    Offers 'structured repositories of information' that help 'maintain context and track progress across multiple sessions'. This fits the project management features and organizational intelligence requirements, including bulk operations and cleanup rules.

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    Memory Bank Server provides a set of tools and resources for AI assistants to interact with Memory Banks. Memory Banks are structured repositories of information that help maintain context and track progress across multiple sessions.
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