List all team members with their roles and status. Returns name, email, role, and whether they've been removed.

Get current billing cycle spending for all team members. Shows spend in dollars, included vs overage, fast premium requests, and spend limits.

Get daily usage data per user: lines added/deleted, applies, accepts, rejects, tabs, requests by mode (composer/chat/agent), models used, and client versions.

Get billing groups with member lists, group-level spend, and daily spend breakdown. Also returns billing cycle dates.

Get granular per-request usage events with model, token counts, costs, and whether the request was chargeable. Supports filtering by user email and date range.

Set a hard spending limit (in dollars) for a specific team member. Use with caution — this will block the user from making requests once the limit is reached.

Get daily active users over time, including breakdowns for CLI, cloud agent, and Bugbot usage.

Get model usage breakdown per day: which models are being used, how many messages, and by how many users. Essential for understanding model adoption and cost drivers.

Get agent edit metrics: suggested vs accepted vs rejected diffs and lines. Shows how effectively the team is using AI-generated code.

Get tab autocomplete usage: suggestions shown vs accepted vs rejected, with line-level detail.

Get MCP (Model Context Protocol) tool usage: which MCP servers and tools are being used, and how often.

Get top file extensions being edited with AI: which file types get the most AI suggestions, accepts, and rejects.

Get Cursor client version distribution across the team: which versions are in use and what percentage of users are on each.

Get command usage analytics: which Cursor commands are being used and how often.

Get plan mode adoption: which models are being used in plan mode and how often.

Get a comprehensive team overview: member count, total spend, top spenders, DAU, and most-used models. This is the best starting point for understanding your team's Cursor usage.

Deep dive into a specific user's usage: their spending, daily usage patterns, recent requests, and model preferences.