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cursor-usage

cursor-usage

by ofershap
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

get_model_usage

Analyze daily AI model usage patterns to identify adoption trends and cost drivers by tracking messages and user activity.

Instructions

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.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_model_usage"
  }
}

Input Schema

NameRequiredDescriptionDefault
startDateNoStart date. Formats: "YYYY-MM-DD", "7d", "30d", "today", "yesterday". Default: "30d"
endDateNoEnd date. Formats: "YYYY-MM-DD", "today", "yesterday". Default: "today"
usersNoComma-separated emails to filter by specific users

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries the burden. It discloses the output breakdown but lacks details on rate limits, authentication, or side effects. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded, no unnecessary words. Efficiently communicates purpose and value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with 3 optional params and no output schema, the description covers the output sufficiently but could explicitly state the return format (e.g., array of objects).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description adds no extra parameter meaning beyond what the schema already provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves daily model usage breakdown, including models, messages, and users. It distinguishes from siblings like get_daily_usage and get_mcp_usage by specifying model focus.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use for understanding model adoption and cost drivers but does not provide explicit when-to-use/when-not-to-use guidance or alternatives among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

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