LearnUpon MCP Server and API by Cotlet

Set enrollment complete in LearnUpon

learnupon_set_enrollment_complete

Read-onlyIdempotent

Set enrollment complete 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_set_enrollment_complete"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 9, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false; the description adds the key behavioral fact that this 'opens nothing by itself' and returns screens/buttons/fields instead of mutating anything, which is exactly the context an agent needs to avoid assuming side effects. It does not mention prerequisites such as an existing LearnUpon Cloud Browser session, so it is not fully complete on behavior.

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?

The definition is front-loaded: purpose first, then the returned content, then the execution path via cotlet_browser_* tools. The final sentence justifying the choice over LearnUpon's official API, including the 'checked 20 Oct 2026' provenance note, is slightly padded but still earns its place as a disambiguation signal.

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?

With no parameters and no output schema, the description carries the burden of explaining what comes back, and it does so (step-by-step screens, buttons, fields). It is sufficiently complete for a simple zero-arg tool, though it omits whether the LearnUpon Cloud Browser must already be open before the returned steps are usable.

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 no parameter meaning to convey and the schema baseline of 4 applies. The description correctly implies no inputs are needed while noting the target is the user's own account.

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 states a specific verb and resource (set enrollment complete in LearnUpon) and immediately clarifies the crucial nuance: this tool does not perform the action but returns step-by-step instructions. That said, the name/title imply execution, so the agent must read the body to discover the tool is an instruction provider, which keeps it just short of a 5.

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 explicitly tells the agent the follow-up path: run the cotlet_browser_* tools in the user's LearnUpon Cloud Browser to actually perform the action, and it explains why the official REST API/reporting MCP server is not an alternative. It lacks an explicit 'do not use this when...' clause (e.g., a native enroll/unenroll sibling), but the positive routing guidance is concrete.

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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