AI agents use create_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 |
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 creates new data (a work experience entry) on the LAPRAS platform in a reversible manner. It does not execute arbitrary code, delete data permanently, or involve financial transactions.
From the tool's definition Tool name is 'create_experience' and description states 'Create a new work experience on LAPRAS'. The verb 'create' and context of adding data to a user profile indicates data creation.
Risk signalsHigh parameter count (11 properties)
Attacks that exploit this kind of access
Create a new 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.
create_experience accepts 10 parameters: end_year, end_month, positions, start_year, description, start_month, position_name, is_client_work, organization_name, client_company_name. Required: end_year, end_month, positions, start_year, start_month, 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 create_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.
create_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 create_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 create_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.
create_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.
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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