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Engram

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    Deploy Server
    Context All Your Agents Can ShareAgents share what they learn, hand off work, and hold it when you switch models. Start with 5 GB free.fulcradynamics.comAd

    TDQS

    B3.1/5.0

    Scored across 87 tools

    Disambiguation2/5

    There are several overlapping tool clusters: recall, query_pure, query_with_momentum, tensor_recall, and summarize all serve retrieval; context_for_file and context_for_edit overlap heavily; update and update_with_tensor_bond have similar write purposes. The descriptions are detailed, but at the set level an agent can easily pick the wrong variant.

    Naming Consistency3/5

    Most tools follow the mcp_engram_<verb>_<noun> pattern, and families like goal_*, thought_tile_*, and var_* are internally consistent. However, mcp_compress_linguistic, mcp_decompress_linguistic, mcp_fibered_linguistic_equivalence, and mcp_linguistic_calculus break the prefix pattern, and noun-only names like mcp_engram_genesis, mcp_engram_stats, and mcp_engram_leg_corpus further blur the convention.

    Tool Count1/5

    With 87 tools, this is far beyond a well-scoped MCP surface. Even a complex memory system does not justify dozens of overlapping retrieval, context, compression, and verification tools; the sheer count will bloat agent context and make selection costly.

    Completeness4/5

    The surface covers the domain unusually well: CRUD on memories, multiple search modes, relation traversal, namespaces, goals, thought tiles, spatial context, import/export, and integrity verification are all present. Minor gaps remain, such as no explicit relation deletion and limited user-model maintenance, but they are not critical for core workflows.

    Maintenance

    ActivityStale
    ResponsivenessNo issues

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