Hicortex - AI Fleet Memory
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TDQS
Scored across 11 tools
While most tools have distinct purposes (get vs search vs delete), hicortex_learnings and hicortex_lessons are exact duplicates — clear ambiguity. Also, hicortex_update and hicortex_delete could overlap if incorrect content might be better corrected than removed, though descriptions mitigate this. hicortex_graph and hicortex_index are distinct but could be confused for related discovery tasks.
All tool names start with 'hicortex_' followed by a single verb or noun (get, delete, search, recent, ingest, update, learnings, lessons, index, identity, graph). Pattern is mostly consistent, but 'learnings' vs 'lessons' are synonyms for the same action, creating redundancy rather than following the verb_noun pattern seen elsewhere (e.g., search is verb, index is noun). Minor inconsistency: verbs for actions, nouns for queries.
With 11 tools, the count is within the ideal range for a memory management system. The tool count feels reasonable for the scope: CRUD operations, search, recent memories, learnings, indexing, identity, and graph exploration. Only redundancy of learnings/lessons slightly inflates the count, but overall it's well-scoped.
The server appears to cover the core lifecycle of memories: create (ingest), read (get, search, recent, index, identity), update, delete. It also includes advanced features like learnings and graph exploration. The only gap is a lack of bulk operations (e.g., delete by filter, list all memories) or a way to export/import, but those are minor and not essential for the stated purpose.