Run the adaptive-mastery promotion logic for the signed-in user (delegates to the web app's update-adaptive-mastery function). Requires sign-in.
| Name | Required | Description | Default |
|---|---|---|---|
| course_id | No | ||
| total_count | Yes | ||
| correct_count | Yes | ||
| words_studied | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=false and destructiveHint=false, but the description adds only that it delegates to a web app function and requires sign-in. No detail on side effects, idempotency, or what 'promotion logic' entails. Given the lack of annotation detail, the description should provide more behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no waste. The key action is front-loaded. However, conciseness sacrifices necessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and high parameter count with zero schema descriptions, the description fails to provide a complete picture. Missing return value, parameter meanings, and usage scenarios make it inadequate for proper tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description offers no explanation for any of the four parameters (course_id, total_count, correct_count, words_studied). This leaves the agent unable to understand how to supply values correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it runs adaptive-mastery promotion logic, which is specific to the tool name. It distinguishes from sibling tools by focusing on promotion logic rather than retrieval or other actions. However, it does not explicitly state the outcome of updating the level.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like get_recommended_next_action or record_word_result. The only context is that it requires sign-in, which is minimal.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clear, distinct purposes with detailed descriptions. A few pairs like study_plan_preview vs get_study_plan_recommendation or get_definition vs explain_word_in_context have subtle overlaps, but descriptions effectively differentiate them.
All tool names use lowercase snake_case with a consistent verb_noun pattern. Some names are longer but follow the same structure. No mixing of conventions, though the variety of verbs is high.
31 tools is on the high side for a vocabulary server. The scope is broad (definition, quizzes, games, progress, parent/tutor features), but many tools are specific, making the set feel heavy. It earns its count but could be trimmed.
The tool surface covers most user needs: learning, testing, progress tracking, parental involvement, and support. Minor gaps like class management or deletion operations exist, but core vocabulary workflows are complete.