Generate up to 20 position-specific practice lessons from a learner's mistakes in a saved game analysis, focusing on the selected side.
Create up to twenty position-specific practice lessons from the learner's mistakes in a saved game analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| side | Yes | ||
| learner_id | Yes | ||
| analysis_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| lessons | Yes | ||
| grouping | Yes | ||
| schema_version | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does disclose a useful cap ('up to twenty') and the origin of the content, but says nothing about side effects on learner progress, permissions required, idempotency, or what happens if the analysis yields fewer mistakes.
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?
A single front-loaded sentence with no filler. Every clause (quantity cap, position-specific, source of the lessons) earns its place.
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?
An output schema exists so return values need not be documented, and the core purpose is stated. However, with no annotations and 0% schema coverage, the missing 'side' semantics and unstated write behavior leave it only minimally adequate.
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%, so the description must compensate. It indirectly implies learner_id ('the learner's mistakes') and analysis_id ('saved game analysis'), but the 'side' parameter is entirely unexplained and its expected values are unspecified, leaving a required field ambiguous.
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 names a specific verb (create) and resource (game lessons) and scopes it precisely: up to twenty position-specific lessons sourced from mistakes in a saved game analysis. This source framing distinguishes it from generic lesson tools like start_lesson or create_repertoire_drill, though it never names an alternative explicitly.
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?
It implies the tool is used after a saved game analysis exists, but gives no explicit when/when-not guidance and does not mention any sibling alternative such as start_lesson or create_repertoire_drill. The agent must infer the workflow position from the phrase 'saved game analysis'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.