Skip to main content
Glama
jagoff

MEMO MCP Server

by jagoff

Related Servers

Alternatives to MEMO MCP Server

  • A
    license
    B
    quality
    A
    maintenance
    Basic Memory is a knowledge management system that allows you to build a persistent semantic graph from conversations with AI assistants. All knowledge is stored in standard Markdown files on your computer, giving you full control and ownership of your data. Integrates directly with Obsidan.md
    17
    6,060 PyPI
    4,041
    AGPL 3.0

Related Servers

  • A
    license
    Not graded
    quality
    A
    maintenance
    Semantic memory for AI agents — local-first MCP server with hybrid search, knowledge graph, contradiction detection, and plan-then-commit consolidation.
    154 npm
    4
    AGPL 3.0
  • F
    license
    Not graded
    quality
    D
    maintenance
    A local-first semantic memory system that enables document ingestion, semantic querying, and knowledge graph traversal via MCP.
    -
  • A
    license
    Not graded
    quality
    D
    maintenance
    Self-hosted semantic memory for AI agents. Save worklogs, decisions, and notes via MCP, then recall them across sessions by meaning rather than keyword. Backed by Postgres + pgvector with local embeddings (multilingual-e5-base).
    1
    MIT
  • A
    license
    Not graded
    quality
    D
    maintenance
    Persistent, semantically-searchable memory for AI agents using local PostgreSQL, pgvector, and Ollama embeddings, exposed via MCP with hybrid retrieval, knowledge graph, and auto-recall hook.
    3 npm
    MIT
  • A
    license
    A
    quality
    A
    maintenance
    Semantic memory MCP server that gives AI agents a self-writing, priority-based memory with local semantic search and automatic contradiction handling. It persists across sessions and projects, entirely on your machine.
    18
    34 npm
    2
    MIT

TDQS

A3.5/5.0

Scored across 60 tools

Disambiguation3/5

Many tools are clearly distinct, but there is real overlap in the save and retrieval clusters: memo_save vs memo_save_text vs memo_offload, and memo_search vs memo_context vs memo_ask vs memo_evidence_pack all require careful reading of the descriptions to choose correctly. The long descriptions help, but the boundaries are not obvious from names alone.

Naming Consistency3/5

Every tool shares the memo_ prefix and uses readable snake_case, but the verb/noun order is inconsistent: core tools use verb_noun (memo_list, memo_search, memo_save_text) while operational tools often use noun_verb (memo_handoff_create, memo_focus_set, memo_attention_add). The naming is still readable, but the mixed conventions prevent a fully predictable pattern.

Tool Count1/5

With 60 tools, this is well beyond the 25+ threshold and into the extreme range. While the domain is broad, many tools are micro-variants or internal plumbing that could be consolidated, and the sheer count will overwhelm an agent's tool-selection surface.

Completeness5/5

The tool surface is unusually thorough: full memory CRUD, search variants, retrieval augmentation, embeddings, history, sessions, maintenance, and an extensive operational journal with focus, handoffs, attention, conflicts, outcomes, procedures, and review lifecycles. There are no obvious dead ends for the stated memory-and-operational-state purpose.

Maintenance

ActivityMaintained
ResponsivenessWithin a week