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LearnUpon MCP Server and API by Cotlet

Invite users in LearnUpon

learnupon_invite_users
Read-onlyIdempotent

Invite users 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_invite_users"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered; the description adds the crucial non-obvious point that it 'opens nothing by itself', explaining why an 'invite' tool is read-only. It also discloses the expected return content (step-by-step screens, buttons, fields) and the required follow-up tool family. Nothing contradicts the annotations.

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

Conciseness4/5

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

Three sentences, front-loaded with what the tool does before the routing instructions and the API caveat. The dated API-justification sentence is longer than strictly necessary but earns its place by explaining why the browser route is required.

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, no-output-schema tool, the description supplies the essential missing context: what it returns (steps), that it mutates nothing, and which tools to use next. Remaining gaps are minor, such as whether the returned steps vary by account role or permissions.

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?

There are zero parameters and the schema is empty with additionalProperties=false, so there is nothing for the description to disambiguate. Baseline 4 applies; the description correctly implies no inputs are needed.

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 names the resource and task (inviting users in LearnUpon) and immediately clarifies the surprising mechanic: it returns step-by-step instructions rather than performing the invite. That resolves the mismatch between the verb in the name and the actual read-only behavior, though an agent must read the second sentence to learn this.

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 states the usage pattern explicitly: call this to get the screens/buttons/fields, then execute with cotlet_browser_* in the Cloud Browser. It also preempts the obvious alternative (LearnUpon's REST API / reporting MCP server) and explains why those don't apply for day-to-day admin work. It does not compare against LearnUpon sibling tools, but the browser-tool routing 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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