Recall MCP
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Alternatives to Recall MCP
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- AlicenseNot gradedqualityBmaintenanceEnables an AI assistant to create, search, and manage locally stored spaced-repetition flashcards, quiz users in chat with front-only prompts, and grade their answers using SM-2 scheduling.MIT
- AlicenseAqualityDmaintenanceProvides spaced-repetition scheduling with the SM-2 algorithm as MCP tools, enabling agents to manage review items like flashcards or vocab locally. It supports adding, grading, and listing due items, plus optional pro features for export and forecast.6MIT
- AlicenseAqualityCmaintenanceEnables agents to perform spaced-repetition learning with FSRS scheduling, including adding cards, reviewing due cards, and grading recall, using a headless SQLite or Postgres backend.934 PyPI2MIT
- AlicenseNot gradedqualityCmaintenanceA lightweight MCP server that adds spaced-repetition flashcards (SM-2) to AI assistants, storing data locally in SQLite and enabling card creation, review, and scheduling.MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI coding agents to store decisions, preferences, and lessons in a local SQLite store, then recall the most relevant ones on demand. Runs fully local — no cloud, API keys, or per-query cost — and also exposes forget and consolidate operations.2Apache 2.0
- AlicenseNot gradedqualityAmaintenanceEnables AI coding agents to save and recall atomic markdown knowledge cards with bidirectional links across sessions, so learning persists and builds over time. Cards live as plain, git-syncable files readable by both agent and human, with no vector database or embeddings required.9 npmISC
TDQS
Scored across 8 tools
Most tools are cleanly separated by resource and action: deck creation/listing, card add/list/delete, due-card retrieval, and review recording each have a distinct purpose. The main soft spot is list_decks vs deck_stats, which both surface counts and due-today numbers, and list_cards vs due_cards, which could be confused at a glance, though the descriptions resolve both.
Seven of eight names follow a predictable verb_noun snake_case pattern (list_decks, create_deck, add_card, list_cards, review_card, delete_card). The two noun-only names, due_cards and deck_stats, deviate from that pattern but remain readable and unambiguous.
Eight tools is well within the ideal range and every one earns its place: deck management, card management, review scheduling, and stats. Nothing feels redundant or missing at the structural level.
The review lifecycle is fully covered (add -> due -> review -> reschedule -> delete), but deck management is incomplete: there is no delete_deck or rename/update_deck, and cards cannot be edited after creation. An agent asked to rename a deck, remove a deck, or fix a typo in a card would hit a dead end.