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agents-learning

by swenbo1-web
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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

    B3.2/5.0

    Scored across 11 tools

    Disambiguation3/5

    The search/read pairs for knowledge, work memory, and candidate references are distinguishable, but the boundaries between formal knowledge vs. work history vs. candidate references remain subtle for an agent. Two separate feedback tools (case_feedback and candidate_reference_feedback) also overlap in purpose, increasing misselection risk.

    Naming Consistency4/5

    All names use snake_case and generally follow a resource_action pattern (knowledge_read, work_memory_search, candidate_reference_feedback, task_update). A few are resource_noun (learning_status, session_context), but the convention remains predictable overall.

    Tool Count5/5

    Eleven tools is well within the appropriate range for a focused learning/reference server. Each tool maps to a distinct operation: status, context, task scope, knowledge access, work-memory access, candidate access, and feedback.

    Completeness3/5

    The surface covers reading, searching, using knowledge, and reporting feedback, but there are no tools to create, update, or delete formal knowledge or candidates. Reliance on external hooks for writes leaves notable lifecycle gaps for a learning system.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources