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litmos-mcp

litmos-mcp

by dbuxton
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

litmos_get_user_learning_paths

Read-onlyIdempotent

Get a user's assigned learning paths and their completion status, including percentage complete and dates.

Instructions

Get all learning paths assigned to a user and their completion status.

Args: params: UserIdInput with: - user_id (str): Litmos encrypted user ID

Returns: str: JSON array of learning path assignments: [{ "Id": str, "Name": str, "Active": bool, "Complete": bool, "PercentageComplete": float, "AssignedDate": str, "StartDate": str | null, "DateCompleted": str | null, "AccessTillDate": str | null }, ...]

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "params": "<params>"
    },
    "name": "litmos_get_user_learning_paths"
  }
}

Input Schema

NameRequiredDescriptionDefault
paramsYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, destructiveHint, and idempotentHint, so safety profile is covered. Description adds value by specifying the return structure with fields like PercentageComplete and DateCompleted, which goes beyond 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?

Description uses a clean docstring format with Args and Returns sections. It is not overly long, though the Returns section is somewhat redundant with the output schema. Still, it is front-loaded and efficient.

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

Completeness5/5

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

Given the tool's complexity (1 parameter, no enums, no nested objects) and the presence of output schema, the description is complete. It covers input, output, and behavioral aspects without missing critical information.

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

Parameters5/5

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

Schema coverage is 0%, but description fully documents the single parameter 'user_id' as 'Litmos encrypted user ID', compensating for the schema's lack of description. Output schema exists, but description also details return fields, further aiding comprehension.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'Get all learning paths assigned to a user and their completion status', which is a specific verb+resource combination. It distinguishes from sibling tools like litmos_get_user_courses (which gets courses) and litmos_get_user_teams.

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

Usage Guidelines3/5

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

No explicit when-to-use or when-not-to-use guidance. The description implies use for learning path assignments, but does not mention alternatives or conditions such as 'use litmos_get_user_courses for course-level results'. Sibling names provide some context but description lacks direct guidance.

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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