Skip to main content
Glama
YashvantHange

SuperMemory MCP

by YashvantHange
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to SuperMemory MCP

No user-submitted related servers found.

    Related Servers

    • Memcell by memcell-ai

      Memcellofficial

      memcell-ai
      A
      license
      Not graded
      quality
      B
      maintenance
      Living memory for AI coding agents (Claude Code, Cursor, Copilot, Codex). Cross-vendor persistent memory, decision recall, and outcome calibration via MCP and hooks.
      807 npm
      Apache 2.0

    • memmd-mcp

      matamong
      A
      license
      B
      quality
      D
      maintenance
      A shared memory layer for AI agents — one memory.md synced across Claude Desktop, Cursor, Claude Code, OpenAI Codex, and any MCP client.
      4
      2
      MIT

    • midas-memory-mcp

      vornicx
      A
      license
      A
      quality
      A
      maintenance
      Local-first, source-traceable memory for AI agents — no LLM at ingest, $0 per message, zero data egress. Gives Claude Code, Cursor, and any MCP client one shared persistent memory with semantic recall, belief revision, selective forgetting, and a provenance guard that blocks acting on stale or unconfirmed memories.
      23
      14
      MIT
    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

    D1.7/5.0

    Scored across 29 tools

    Disambiguation2/5

    Many tools have overlapping or ambiguous names, especially the 'learn.*' tools with empty descriptions (e.g., learn.analytics, learn.evaluate) which are indistinguishable from each other. Additionally, 'retrieve' and 'learn.retrieve' appear to serve similar purposes, causing confusion.

    Naming Consistency2/5

    Naming conventions are mixed: some use underscore (add_policy), some use dot notation (learn.analytics), and some are single words (reflect). The 'learn.' prefix is applied inconsistently across tools, and verb_noun patterns are not uniformly followed.

    Tool Count2/5

    29 tools is excessive for a server that appears to manage policies, skills, and lessons. Many tools seem redundant (e.g., multiple learn.* tools) and could be consolidated. The scope does not justify this many distinct operations.

    Completeness3/5

    Core functionalities like adding, retrieving, and validating are present, but there are missing operations such as updating or deleting policies/skills. The learn.* tools are undocumented, leaving potential gaps in the learning pipeline unaddressed.

    Maintenance

    ActivityInactive
    ResponsivenessUnresponsive

    Resources