JauMemory MCP Server
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- AlicenseNot gradedqualityAmaintenancePersistent memory for AI assistants — store insights, decisions, and knowledge that survives across conversations.12Apache 2.0
TDQS
Scored across 50 tools
There are multiple overlapping and confusing tools like 'search', 'fetch', and 'recall' where 'search' says it's discovery-only and redirects to 'recall', while 'fetch' returns route documentation. Tools like 'remember', 'recall', 'forget', 'update' suggest a CRUD line but are mixed with uncorrelated agents, skills, vault, and Berrry tools. Several tools have ambiguous boundaries (e.g., 'agent_error_learning' vs 'agent_reflection', or 'remember' vs 'analyze' vs 'consolidate').
The naming is highly inconsistent. Some tools use snake_case (memory_stats, list_agents, create_agent), some use lowercase (remember, recall, forget), and some have redundant or inconsistent prefixes like 'agent_error_learning' vs 'agent_reflection' vs 'agent_memory'. There's also a 'berrry_' prefix for Berrry-specific tools mixed with others, and 'tool_' for registry tools. No consistent verb_noun pattern is maintained across the server.
With 50 tools, the server is extremely heavy for what appears to be a memory server. The large number suggests it's trying to do too much—mixing memory management, agent orchestration, skill workflows, vault storage, and even an app deployment helper (berrry_*). This volume feels bloated rather than focused, and many tools could be removed or modularized into separate servers.
For the memory domain, core operations exist (remember, recall, forget, update, consolidate) but feel randomly extended with agent, skill, vault, and app deployment tools. The inclusion of 'berrry_create_tool' and 'berrry_register_tool' is a major scope creep that harms the server's focus. It's unclear if the agent and skill tools are complete—they reference many sub-actions (start, complete, list) but are bundled into single tools with actions, which hides gaps.