AI agents use update_experience to create or update resources in Lapras — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Lapras environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
end_year | number | Yes | End year (0 if ongoing) |
end_month | number | Yes | End month (0 if ongoing) |
positions | array | Yes | List of position type IDs - multiple selections are allowed. Please set relevant position types. |
start_year | number | Yes | Start year |
description | string | — | Detailed description of the experience (Markdown format) |
start_month | number | Yes | Start month |
experience_id | number | Yes | ID of the experience to update |
position_name | string | — | Position title |
is_client_work | boolean | Yes | Whether this is client work (Set to true when the affiliated company and the project client are different, such as in contract development companies) |
organization_name | string | Yes | Name of the organization |
client_company_name | string | — | Client company name (required only when is_client_work is true) |
Parameters from the server's own tool schema.
This tool modifies user profile data reversibly by updating work experience records. It is not destructive (the original can be reverted), not financial (no money involved), and not execute/read-only. The severity is medium because misuse could alter a user's professional profile which affects their reputation and job opportunities, but changes are not permanent and can be corrected.
From the tool's definition Tool name 'update_experience' and description 'Update a work experience on LAPRAS' explicitly indicates modification of existing data (user's professional profile/CV).
Risk signalsHigh parameter count (12 properties)
Attacks that exploit this kind of access
Update a work experience on LAPRAS(https://lapras.com). You can check the result at https://lapras.com/cv. It is categorised as a Write tool in the Lapras MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
update_experience accepts 11 parameters: end_year, end_month, positions, start_year, description, start_month, experience_id, position_name, is_client_work, organization_name, client_company_name. Required: end_year, end_month, positions, start_year, start_month, experience_id, is_client_work, organization_name. The full parameter table on this page comes from the server's own tool schema.
Register the Lapras MCP server in PolicyLayer and add a rule for update_experience: 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.
update_experience is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the update_experience 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 update_experience. 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.
update_experience 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.
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