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LearnWorlds MCP Server and API by Cotlet

Suspend user in LearnWorlds

learnworlds_suspend_user
Read-onlyIdempotent

Suspend user in the user's own LearnWorlds account from ChatGPT, Claude or any AI agent. Returns how to do it in LearnWorlds step by step (screens, buttons, fields) and opens nothing by itself; then do it with the cotlet_browser_* tools in the user's LearnWorlds Cloud Browser. LearnWorlds's official API: LearnWorlds has a REST API (v2) for courses, users, bundles, subscriptions, promotions, payments and certifications, but it comes with the Learning Center plan and higher, and you need your own code to use it (checked 5 Nov 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": "learnworlds_suspend_user"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

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, and the description reinforces this with "opens nothing by itself", so there is no contradiction. It adds genuine value by disclosing the return shape (screens, buttons, fields) and the handoff to the browser tools, which annotations cannot convey. The plan/API note is contextual filler rather than behavior, keeping it below a 5.

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 core message (returns instructions, does not act, then use cotlet_browser_*) is front-loaded and useful. However, "from ChatGPT, Claude or any AI agent" is promotional filler, and the trailing API/plan-tier note with a checked-on date is tangential to invoking this tool correctly.

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 zero-parameter, read-only tool that returns instructional text, the definition covers what the tool produces and what to do next, which is what an agent needs to act correctly. There is no output schema, so the prose description of the return value is appropriate and sufficient. Only the vague name-to-behavior mapping is a minor residual gap.

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 takes zero parameters, so there is nothing for the description to disambiguate; the schema also forbids additional properties. Baseline 4 applies since no parameter meaning is missing.

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 first sentence names a specific verb and resource ("Suspend user in the user's own LearnWorlds account") and the second clarifies the key non-obvious fact that the tool returns step-by-step instructions rather than performing the suspension. It is distinguishable from siblings like learnworlds_list_users or learnworlds_update_tags. It loses a point because the name 'learnworlds_suspend_user' implies the mutation happens here, and only the body text corrects that.

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?

Explicitly routes the agent to the correct follow-up: "then do it with the cotlet_browser_* tools in the user's LearnWorlds Cloud Browser", which is exactly the alternative an agent needs to know. It also explains why this route exists (the REST API requires the Learning Center plan and your own code). It stops short of stating when not to use it, e.g., whether to prefer the native API path when the plan allows it.

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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