Skip to main content
Glama

enroll_user_product

Enroll a user in a course, bundle, or subscription product using user ID or email. Specify product ID, type, price, and optional justification or duration for subscriptions.

Instructions

🟡 WRITE · creates data · Users · POST /v2/users/{id}/enrollment

Enroll user to product

Enroll user to product, regarding course, bundle, manual subscription

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesUser Id or email (encoded string)
bodyNoRequest body (application/json).
Behavior3/5

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

Annotations indicate readOnlyHint=false and destructiveHint=false, and the description states 'WRITE · creates data', which is consistent and adds basic context. However, beyond stating it creates data, it discloses no further behavioral traits (e.g., side effects, error scenarios, idempotency). With annotations already covering the core safety profile, the description adds minimal extra value.

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 relatively short but contains redundant repetition of 'Enroll user to product'. The header line with method and endpoint is useful, but the main text could be streamlined. It earns its place but is not optimally concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has a complex nested body and no output schema, yet the description does not explain return values, error responses, or prerequisites. For a write operation, agents need to know what to expect on success or failure. The description is incomplete for effective use.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all parameters well. The description adds 'regarding course, bundle, manual subscription', which hints at productType's enum values but is slightly inaccurate (schema uses 'subscription' not 'manual subscription'). It does not enhance understanding beyond the schema.

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 clearly states 'Enroll user to product' and mentions specific product types (course, bundle, subscription). This verb+resource combination is distinct from sibling tools like unenroll_user_product or add_user_seat_offering. However, it repeats the same phrase twice and does not explicitly differentiate from other enrollment-related siblings.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. For example, it does not mention that the user must exist or that the product must be available. Without such context, an AI agent may misuse the tool.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ohneben/Learnworlds-MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server