download_file
AI agents call download_file to retrieve information from Xiaoya Teacher without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
The tool name suggests fetching/retrieving file content without modification or deletion. No description is provided, which reduces confidence, but the verb 'download' is consistent with Read operations. Given the context of an educational management platform (course resources, assignments), downloading likely retrieves existing course materials or student work.
From the tool's definition Tool name 'download_file' indicates retrieval of file data; description is empty, limiting confidence.
Attacks that exploit this kind of access
download_file. It is categorised as a Read tool in the Xiaoya Teacher MCP Server, which means it retrieves data without modifying state.
Register the Xiaoya Teacher MCP server in PolicyLayer and add a rule for download_file: 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 Xiaoya Teacher. Nothing to install.
download_file 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 download_file 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 download_file. 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.
download_file is provided by the Xiaoya Teacher MCP server (sav1our520/xiaoya-teacher-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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