cursor-usage
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| CURSOR_API_KEY | Yes | Your Cursor Enterprise Admin API key |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_team_membersA | List all team members with their roles and status. Returns name, email, role, and whether they've been removed. |
| get_spendingB | Get current billing cycle spending for all team members. Shows spend in dollars, included vs overage, fast premium requests, and spend limits. |
| get_daily_usageB | 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_groupsA | Get billing groups with member lists, group-level spend, and daily spend breakdown. Also returns billing cycle dates. |
| get_usage_eventsA | 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_spend_limitA | 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_dauB | Get daily active users over time, including breakdowns for CLI, cloud agent, and Bugbot usage. |
| get_model_usageA | 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_editsB | Get agent edit metrics: suggested vs accepted vs rejected diffs and lines. Shows how effectively the team is using AI-generated code. |
| get_tabsB | Get tab autocomplete usage: suggestions shown vs accepted vs rejected, with line-level detail. |
| get_mcp_usageB | Get MCP (Model Context Protocol) tool usage: which MCP servers and tools are being used, and how often. |
| get_file_extensionsB | Get top file extensions being edited with AI: which file types get the most AI suggestions, accepts, and rejects. |
| get_client_versionsA | Get Cursor client version distribution across the team: which versions are in use and what percentage of users are on each. |
| get_commandsC | Get command usage analytics: which Cursor commands are being used and how often. |
| get_plansB | Get plan mode adoption: which models are being used in plan mode and how often. |
| get_team_overviewA | 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. |
| get_user_deep_diveA | Deep dive into a specific user's usage: their spending, daily usage patterns, recent requests, and model preferences. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 17 tools
Most tools have distinct purposes (e.g., get_agent_edits vs get_tabs vs get_model_usage). However, get_daily_usage and get_usage_events may overlap slightly, and get_spending/get_billing_groups both deal with costs, but descriptions clarify differences.
All tools follow a consistent get_noun pattern in snake_case (e.g., get_daily_usage, get_team_members). The only outlier is set_spend_limit which uses 'set', which is appropriate for a mutation.
17 tools is slightly high but well-justified for a comprehensive analytics server. Each tool covers a distinct metric, and the count is within reasonable bounds.
Covers most key areas: user activity, model usage, spending, team members, and even includes a mutation (set_spend_limit). However, lacks tools for inviting/removing team members or managing billing settings.