Skip to main content
Glama
Sign In
MCP MITM Mem0

MCP MITM Mem0

by terrymunro
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Related Servers

Alternatives to MCP MITM Mem0

No user-submitted related servers found.

    Related Servers

    • semantic-memory

      GFYURI
      A
      license
      Not graded
      quality
      C
      maintenance
      Persistent memory with semantic search for Claude and MCP-compatible clients, storing context that survives conversations and can be retrieved intelligently.
      1
      MIT

    • Claude Soul

      LinaBianchi
      A
      license
      Not graded
      quality
      A
      maintenance
      Enables Claude Code to retain cross-session memory of facts, decisions, and lessons with semantic search, while tracking user corrections as behavioral patterns and evolving self-improving frameworks over time. All processing runs locally through an MCP server and hooks, requiring no cloud service or API key.
      6 npm
      MIT

    • mcp-openmemory

      baryhuang
      A
      license
      A
      quality
      D
      maintenance
      Enables Claude to remember conversations and learn over time by storing and recalling messages, memory abstracts, and recent history using a local SQLite database.
      4
      10 npm
      70
      MIT

    • Melchizedek

      louis49
      A
      license
      A
      quality
      C
      maintenance
      Persistent memory for Claude Code. Automatically indexes every conversation and provides production-grade hybrid search (BM25 + vectors + reranker) via MCP tools. 100% local, zero config, zero API keys, zero invoice.
      16
      30 npm
      7
      MIT
    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

    B3.3/5.0

    Scored across 6 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: add, delete, list, and search memories are distinct CRUD operations, while analyze_conversations and suggest_next_actions provide separate analytical functions. The descriptions reinforce this separation with specific use cases like 'AUTO-STORE' or 'AUTO-SUGGEST'.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case (e.g., add_memory, analyze_conversations). The verbs are clear and descriptive (add, analyze, delete, list, search, suggest), and the nouns align well with the memory/conversation domain, making the set predictable and readable.

    Tool Count5/5

    With 6 tools, this server is well-scoped for managing conversation memories and providing insights. Each tool earns its place by covering essential operations (CRUD for memories, analysis, and suggestions) without being overly sparse or bloated, fitting a typical utility server range.

    Completeness4/5

    The tool set provides strong coverage for memory management (add, delete, list, search) and analytical functions (analyze, suggest), with no dead ends. A minor gap exists in update functionality for memories (e.g., edit_memory), but agents can work around this by deleting and re-adding, and the core workflows are well-supported.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources