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Lyellr88

marm-memory

by Lyellr88
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Python

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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A4.1/5.0

    Scored across 16 tools

    Disambiguation5/5

    Each tool targets a distinct resource or action: memory distillation, compaction, recall, logging, notebook workflows, summaries, code-graph operations, code lookup/context, and concept-graph operations. Apparent overlaps such as code_lookup vs code_context and smart_recall vs concept_recall are explicitly differentiated in the descriptions.

    Naming Consistency4/5

    All names use lowercase snake_case with a marm_ prefix, which is highly consistent. Most follow marm_<domain>_<action>, but some noun-phrase names (marm_code_context, marm_notebook, marm_summary) and bare marm_delete deviate slightly from a strict verb_noun pattern.

    Tool Count4/5

    16 tools is slightly above the ideal 3-15 range, but the server covers several cohesive subdomains (memory, logs, notebook, code graph, concept graph), so most tools earn their place. It is not excessive for the breadth of functionality.

    Completeness4/5

    The surface covers memory creation, compaction, recall, logging, notebook workflows, code-graph indexing/lookup/trace/impact, and concept extraction/recall. Minor gaps remain: no direct update/delete for individual stored memories, and no obvious way to browse memories without a query.

    Maintenance

    ActivityActive
    ResponsivenessWithin a week

    Resources