Get a full summary of your current academic status — assignments, grades, habits, and upcoming events.
AI agents call get_study_summary to retrieve information from Shiori MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves and aggregates existing academic data for display/query purposes. It performs no data modification, deletion, code execution, or financial operations. The worst-case misuse by an AI agent would be querying user academic information without authorization, which is a confidentiality concern but not a blast-radius risk like Execute or Destructive tools.
From the tool's definition Tool name is 'get_study_summary' and description states 'Get a full summary of your current academic status' — the verb 'Get' and the read-only nature of aggregating existing data (assignments, grades, habits, upcoming events) without modification.
Documented attack patterns abuse exactly the kind of access get_study_summary gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Shiori MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for get_study_summary:
{
"version": "1",
"default": "deny",
"tools": {
"get_study_summary": {}
}
}get_study_summary is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Get a full summary of your current academic status — assignments, grades, habits, and upcoming events. It is categorised as a Read tool in the Shiori MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Shiori MCP Server MCP server in PolicyLayer and add a rule for get_study_summary: 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 Shiori MCP Server. Nothing to install.
get_study_summary is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the get_study_summary 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 get_study_summary. 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.
get_study_summary is provided by the Shiori MCP Server MCP server (kaorii-ako/shiori-v1). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Shiori MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
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