cursor-usage

get_user_deep_dive

Analyze a specific user's spending, daily usage patterns, recent requests, and model preferences to understand their behavior.

Instructions

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

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "email": "<email>"
    },
    "name": "get_user_deep_dive"
  }
}

Input Schema

Name	Required	Description	Default
`email`	Yes	User email to analyze
`startDate`	No	Analytics date range start (default: "7d")

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.2Jun 28, 2026

TDQS

A3.6/5.0

Behavior3/5

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

With no annotations, the description must disclose behavior. It outlines the type of analysis (spending, patterns, requests, preferences) but does not clarify if it is read-only or if there are any restrictions. This is adequate but not fully transparent.

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?

The description is a single, clear sentence with no unnecessary words. It is efficient and front-loaded.

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?

Given no output schema, the description compensates by listing the areas covered. However, it does not provide structure for the return data. With two simple parameters, this is mostly complete but could be more detailed.

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 coverage is 100%, with descriptions for both parameters. The description adds no additional meaning beyond what the schema provides, so the baseline score of 3 is appropriate.

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 provides a 'deep dive' into a specific user's usage, listing specific aspects like spending, daily patterns, recent requests, and model preferences. This distinguishes it from sibling tools that might cover aggregate data or different scopes.

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

Usage Guidelines2/5

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

The description does not provide guidance on when to use this tool versus alternatives like get_spending or get_daily_usage. It implies focus on a single user but lacks explicit when-to-use or when-not-to-use instructions.

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

Deploy Server