second-brain
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- AlicenseNot gradedqualityAmaintenanceEnables AI agents to share persistent, self-improving memory by ingesting, classifying, synthesizing, and retrieving knowledge from Obsidian vaults, code, documents, transcripts, bookmarks, and sessions through MCP and REST interfaces with token-optimized search.5 npmMIT
- AlicenseNot gradedqualityBmaintenanceGives any MCP-capable AI agent long-term memory from a self-hosted Obsidian vault, exposing tools to read, search, list and write markdown notes over MCP or a REST API with keyword plus semantic search. Agent writes are confined to a drafts inbox so they never overwrite your own notes.2 npmMIT
- AlicenseAqualityBmaintenanceProvides a shared, persistent memory layer for AI assistants, letting them read and write to a local Obsidian vault through MCP with validation, secret rejection, and deduplication.8Apache 2.0
- FlicenseNot gradedqualityBmaintenanceServes as a unified MCP gateway to an Obsidian vault, enabling AI agents to perform hybrid lexical/vectorial semantic search, explore note graphs, and write through consent-based intent batches with conflict detection.-
- AlicenseNot gradedqualityDmaintenanceEnables LLM assistants and agents to query and manage a personal markdown vault as a graph-based knowledge base using hybrid retrieval, with tools for searching, editing, tagging, and recalling memories across MCP-compatible clients.30 PyPI4MIT
- AlicenseNot gradedqualityDmaintenanceLocal-first knowledge backend for AI agents that connects MCP hosts to an Obsidian-compatible vault with indexed retrieval, token-budgeted memory recall, and secure ingestion.1MIT
TDQS
Scored across 12 tools
Each tool targets a distinct resource plus action: notes (remember/search/node_get/node_update/recall), the task queue (submit/list/update), messaging (send/get/mark_read), and health. The only naturally confusable pair, brain_search vs brain_recall, is explicitly separated in the descriptions (ranked note list vs assembled context block within a token budget), and remember vs update is likewise clarified. No two tools appear to do the same thing.
All names are snake_case under a uniform `brain_` prefix, and most follow a resource-then-verb form (brain_tasks_submit, brain_messages_mark_read). Minor deviations exist: bare verbs (brain_remember, brain_search, brain_recall, brain_health) and a singular/plural resource split (brain_node_get vs brain_tasks_list). Readable and predictable overall, but not a single rigid pattern.
Twelve tools is well inside the healthy range and each one maps to a real operation across the three sub-domains (memory, task queue, messaging). Nothing looks like filler, and the surface is small enough that an agent can enumerate it easily.
The memory lifecycle (create, read, update, search, recall) and the task-queue and inbox lifecycles are covered end to end, including dependency release on completion. Gaps remain: no way to delete or archive a note or message, and no single-task fetch (you must list and filter), though status changes via brain_tasks_update give a partial workaround.