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    • A
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      Self-hosted memory and governance layer for AI coding agents. 28 MCP tools with hybrid search, structured knowledge capture, behavioral nudges, and git-native storage. Zero cloud dependencies.
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    • A
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      MCP memory server for AI coding agents to remember decisions, patterns, and bugs between sessions. Provides persistent memory with 37 MCP tools, multi-session coordination, and token-efficient recall.
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    • A
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      Persistent codebase knowledge layer for AI agents. Pre-digests codebases into structured knowledge (symbols, dependency graphs, co-change patterns, architectural decisions) and serves via MCP. 28 languages, 14 tools, ~85% token reduction.
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    • F
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      Persistent, self-curating memory for coding agents. It enables local, zero-cost context recall through MCP tools with hybrid retrieval and autonomous consolidation.
      -
    • A
      license
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      maintenance
      Enables AI coding agents to maintain persistent, cross-session memory of codebase architecture, naming conventions, and decisions through MCP tools. Eliminates repetitive project re-explanation by automatically injecting stored context into every session with local-first SQLite storage and optional team sharing capabilities.
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    TDQS

    B3.4/5.0

    Scored across 32 tools

    Disambiguation4/5

    Most tools have distinct purposes with clear boundaries, such as analyze_codebase for scanning, create_architecture for design, and diff_snapshots for comparison. However, some overlap exists between tools like get_architecture and list_architectures, or index_code and index_code_continue, which could cause minor confusion in selection.

    Naming Consistency5/5

    Tool names follow a highly consistent verb_noun pattern throughout, such as analyze_codebase, create_architecture, and diff_snapshots. This predictability makes it easy for agents to understand and navigate the toolset without ambiguity.

    Tool Count3/5

    With 32 tools, the count feels heavy for a single server, potentially overwhelming for agents to manage efficiently. While the domain of code analysis and architecture management is broad, the toolset could benefit from consolidation to reduce complexity and improve usability.

    Completeness5/5

    The toolset provides comprehensive coverage for codebase analysis, architecture management, and decision tracking, including CRUD operations (e.g., create, get, update, list architectures) and advanced features like diffing, linting, and exporting. No obvious gaps are present, ensuring agents can handle full workflows without dead ends.

    Maintenance

    ActivityInactive
    ResponsivenessUnresponsive