Read what Luca wants to learn

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

Annotations already cover readOnly/idempotent/non-destructive/openWorld=false, so the bar is lower, yet the description adds real context: required scopes (learning:read, luca:read), full_content data sensitivity, and the redaction behavior where the worked-example pair is blanked for redacted-tier keys. The idempotency line largely restates the idempotentHint annotation.

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?

The core purpose is front-loaded in the first sentence, with scope/sensitivity metadata following in a compact block. Slightly marred by the 'Does not require idempotency' line, which is redundant with idempotentHint=true and adds little.

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 an output schema present, return values need not be explained, and the description still covers auth scopes, sensitivity tier, redaction behavior, and workspace-default overrides. What is missing is only the when-to-use framing against sibling learning tools.

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 coverage is 100%, so the baseline is 3, but the description adds meaning the schema lacks: both params override a default workspace configured by environment, and they are alternatives to each other. That is genuine value beyond the per-field 'Optional Luca workspace id override' text.

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 a specific verb and resource: reading the three profile questions Luca still owes an answer to, plus the underlying operation (GET /api/teach-luca). This is more precise than a generic 'learning' tool, though it does not explicitly contrast itself with the other luca_learning_* siblings (framework performance, A/B tests, proposals).

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?

Usage is implied by the purpose and the workspace-override note, but there is no explicit 'use this when...' guidance or named alternative. Given the crowded luca_learning_* and luca_coach_* families, a sentence on when this panel is the right call versus those siblings would help.

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