LearnUpon MCP Server and API by Cotlet

Disable user in LearnUpon

learnupon_disable_user

Read-onlyIdempotent

Disable user in the user's own LearnUpon account from ChatGPT, Claude or any AI agent. Returns how to do it in LearnUpon step by step (screens, buttons, fields) and opens nothing by itself; then do it with the cotlet_browser_* tools in the user's LearnUpon Cloud Browser. LearnUpon's official API: LearnUpon has a REST API and a reporting MCP server, but neither covers day-to-day admin work from an AI agent (checked 20 Oct 2026).

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": "learnupon_disable_user"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 9, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description reinforces this by stating it 'opens nothing by itself' and returns step-by-step instructions, which is critical given the potentially destructive-sounding name. It adds useful behavioral context beyond annotations but does not mention rate limits or authentication.

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

Conciseness3/5

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

The description is reasonably front-loaded but includes extraneous marketing-style phrasing ('from ChatGPT, Claude or any AI agent') and a dated caveat about the official API. Every sentence is not strictly necessary; the core information could be conveyed more tightly.

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 the tool's simplicity (no parameters, no output schema), the description sufficiently explains what it returns (step-by-step screens, buttons, fields) and how to use it with browser tools. It also clarifies that no direct action is taken. A minor gap is the exact format of the returned instructions, but overall it is complete enough for correct invocation.

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

Parameters4/5

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

The tool has zero parameters, and the schema is empty with 100% coverage. No parameter description is needed, and the description correctly omits parameter details. Baseline 4 applies for a no-parameter tool.

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

Purpose4/5

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

The description specifies that the tool provides step-by-step instructions for disabling a LearnUpon user, not performing the disable itself. It distinguishes this from the official LearnUpon API. However, the name 'disable_user' could mislead an agent into thinking it performs the action, and the description could more explicitly name sibling tools it replaces or supports.

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

Usage Guidelines4/5

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

It clearly explains when to use the tool: to get instructions for disabling a user, then follow up with cotlet_browser_* tools. It also explains why the official API is not used. It lacks explicit exclusions or alternatives among the learnupon_* siblings, but the unique purpose is clear.

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

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