srs-mcp
Enables 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.
README
srs-mcp
Agent-agnostic MCP server for spaced-repetition learning — no Anki GUI, no Xvfb, no AnkiConnect. Bring your own agent; this brings the card box + the scheduler.
It wraps FSRS (the Free Spaced Repetition Scheduler, the same algorithm modern Anki uses) around a tiny SQLite store, so an agent can author cards, see what's due, and record recall — entirely headless.
Why not headless Anki?
Driving the Anki desktop app headless means Qt + a virtual framebuffer
(Xvfb) + the AnkiConnect add-on — brittle and version-coupled. The
anki PyPI package can drive a real .anki2 collection GUI-less if you
need interop with your phone's Anki. But if you just want spaced
repetition behind an API, you don't need Anki at all: FSRS is a library,
and this server is ~200 lines around it.
Tools
add_card(front, back, deck="default") -> {card_id, due}— author + schedule a carddue_cards(deck=None, limit=20) -> [{card_id, front, back, deck, due}]— what's due nowgrade_card(card_id, rating) -> {card_id, rating, next_due, reps}— record recall (again/hard/good/easy, or 1-4)list_cards(deck=None, limit=50)— overview regardless of due datedelete_card(card_id)— remove one (reset / cleanup)stats(deck=None) -> {total, due_now, reviews, decks}
The review loop: due_cards → quiz the user with front → check against
back → grade_card. FSRS computes the next due date from the rating.
Run
uv sync
# HTTP (default; for Railway / remote agents)
PORT=8000 uv run srs-mcp
# or stdio (local agent)
MCP_TRANSPORT=stdio uv run srs-mcp
Storage
Two backends, chosen at startup:
- Postgres (shared deck) — set
SRS_DATABASE_URL(orDATABASE_URL) to a Postgres connection string (e.g. a Neon DB). Every deployment that points at the same URL reads/writes one shared deck, so you can add and review cards from anywhere (local, Railway, etc.). FSRS card ids are large, so thecards.card_idcolumn isBIGINTon Postgres. Requires thepsycopgdependency (already declared). - SQLite (fallback) — when no
*DATABASE_URLis set, cards live in a SQLite file atSRS_DB(default./srs.db). Single-host / offline. In a SQLite-on-Railway setup, mount a volume at/dataand keepSRS_DB=/data/srs.dbso the box survives redeploys.
The schema is identical (table cards) and auto-created on first use.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.