Get one major's full ROI detail

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

No annotations are provided, so the description bears full responsibility. It details the data returned but omits behavioral traits such as read-only nature, auth requirements, rate limits, or error handling. This is a moderate gap given no annotation support.

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?

Three sentences: first states the core purpose and lists outputs, second clarifies slug acceptance, third directs to sibling for discovery. No fluff, all information 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?

Given one parameter and no output schema, the description is largely complete. It explains inputs and outputs thoroughly. Slight lack of coverage on error scenarios or read-only nature, but acceptable.

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% with one parameter. The description adds value beyond the schema by clarifying that the tool accepts a plain name and will slugify it, which is not obvious from the property description.

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 clearly states the tool returns a full lifetime-ROI record for one major by slug, listing specific data points (ROI distribution, break-even age, etc.). It distinguishes itself from the sibling list_majors tool for discovering slugs.

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?

Explicitly says 'use list_majors to discover exact slugs', providing clear guidance on when to use which tool. Also notes that it accepts a plain name and will slugify it, setting expectations.

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