study_session
Find due cards and apply review-state actions such as answering, suspending, unsuspending, resetting, and relearning cards. Use this for scheduling and review flow, not for editing note content or reorganizing decks. Several operations mutate card state in Anki immediately;
This record as markdown: /tools/io-github-arielbk-anki-mcp/study-session.md
What study_session does on Anki
AI agents invoke study_session to trigger actions in Anki. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
Why study_session is rated High
This tool triggers external operations on Anki card state (answering, suspending, resetting, relearning) that take effect immediately. While some sub-actions are reversible (unsuspending), others like resetting scheduling data are difficult to undo.
From the tool's definition 'apply review-state actions such as answering, suspending, unsuspending, resetting, and relearning cards' and 'Several operations mutate card state in Anki immediately'
Attacks that exploit this kind of access
The rule that runs study_session safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Anki, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For study_session, this is the rule to start with:
study_session stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Anki, apply this rule, and every study_session call is checked against it from then on.
Questions about study_session
Find due cards and apply review-state actions such as answering, suspending, unsuspending, resetting, and relearning cards. Use this for scheduling and review flow, not for editing note content or reorganizing decks. Several operations mutate card state in Anki immediately;. It is categorised as a Execute tool in the Anki MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Anki MCP server in PolicyLayer and add a rule for study_session: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Anki. Nothing to install.
study_session is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the study_session rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for study_session. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
study_session is provided by the Anki MCP server (@arielbk/anki-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
More on Anki, and thousands of servers like it.
This server
Across the catalogue