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qcdoc-mem

by wychenzhou
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Python
Hybrid

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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

    A4.3/5.0

    Scored across 10 tools

    Disambiguation5/5

    Each tool targets a distinct operation: read whole memory, search, context injection, manual write, automatic extraction, update, delete, bulk forget, flush, and status. The only near-overlap (memory_get vs memory_context) is explicitly disambiguated in descriptions, and memory_remember vs memory_remember_conversation clearly separates manual from backend-extracted storage.

    Naming Consistency4/5

    All tools share the memory_ prefix and use snake_case, mostly following a memory_<verb> pattern. memory_context is a noun-style endpoint and memory_status/remember_conversation vary slightly in structure, but the overall pattern remains predictable.

    Tool Count5/5

    10 tools is well-scoped for a memory backend: read, search, context load, manual write, automatic extraction, update, delete, full erase, flush, and status. Each tool earns its place with no redundant bloat.

    Completeness5/5

    Covers the full lifecycle of persistent facts: create (remember/remember_conversation), read (get/search/context), update, delete, plus admin operations (flush/status) and destructive forget. No obvious dead ends for the stated purpose.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources