Skip to main content
Glama

iSpring Learn (iSpring LMS) MCP Server and API by Cotlet

Get learner progress report in iSpring Learn (iSpring LMS)

ispring_get_learner_progress_report
Read-onlyIdempotent

Get learner progress report in the user's own iSpring Learn (iSpring LMS) account from ChatGPT, Claude or any AI agent. Returns how to do it in iSpring Learn (iSpring LMS) step by step (screens, buttons, fields) and opens nothing by itself; then do it with the cotlet_browser_* tools in the user's iSpring Learn (iSpring LMS) Cloud Browser. iSpring Learn (iSpring LMS)'s official API: iSpring Learn has a REST API, but only with the Business subscription, and no official MCP server (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": "ispring_get_learner_progress_report"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description still contributes beyond them by stating 'opens nothing by itself', which prevents an agent from assuming the report is fetched, plus the API/rate context (REST API only on Business tier, no official MCP server). That is useful but not deep: nothing is said about output format beyond 'step by step' or about failure modes.

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 workflow is front-loaded and the 'opens nothing by itself' caveat is well placed, but the repeated 'iSpring Learn (iSpring LMS)' branding appears three times and pads the text without adding meaning. The API caveat sentence is relevant but the phrasing could be tighter.

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 saying what comes back, and it does so ('how to do it... step by step (screens, buttons, fields)'). It also explains how to act on the result via the browser tools, leaving little an agent needs to infer.

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 the baseline is 4; there is no argument meaning to add and the description does not need to explain any.

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 a specific resource (learner progress report) and clarifies the key nuance that the tool returns step-by-step instructions rather than the report data: 'Returns how to do it... step by step (screens, buttons, fields) and opens nothing by itself'. The verb 'Get' in the name is slightly at odds with what it actually returns, but the body corrects that immediately, so an agent can tell what it gets.

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 gives an explicit two-step workflow: call this first to learn the screens/buttons/fields, then perform the actions with the cotlet_browser_* tools in the Cloud Browser. It does not state when NOT to use it or point at sibling API tools like ispring_list_enrollments, but the sequencing guidance is clear.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources