AI agents call get_want_to_do to retrieve information from Lapras without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves career aspiration information from the LAPRAS platform. The verb 'Get' and the context of querying user profile data (career aspirations) classify this as a Read operation. There are no side effects, data modifications, or external actions triggered.
From the tool's definition Tool name 'get_want_to_do' and description 'Get career aspirations' indicate data retrieval with no modification, deletion, or execution of external operations.
Attacks that exploit this kind of access
Get career aspirations（今後のキャリアでやりたいこと） on LAPRAS(https://lapras.com). It is categorised as a Read tool in the Lapras MCP Server, which means it retrieves data without modifying state.
Register the Lapras MCP server in PolicyLayer and add a rule for get_want_to_do: 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 Lapras. Nothing to install.
get_want_to_do 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_want_to_do 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_want_to_do. 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_want_to_do is provided by the Lapras MCP server (@lapras-inc/lapras-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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