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

With no annotations, the description carries the full burden and does well: it discloses vote weighting by job reputation, that zero-reputation and internal votes weigh 0, and the self-vote prohibition. It omits whether an existing vote can be changed/withdrawn and what the call returns, but the core behavioral rules are unusually explicit.

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?

One front-loaded sentence with no wasted preamble; the parenthetical packs the operative rules compactly. It is dense but every clause carries a real constraint.

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?

For a 3-param mutation with no annotations and no output schema, the description covers the essential behavioral rules an agent needs before voting. It stops short of describing auth expectations or the effect on the lesson's score, which is a minor gap.

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 67%, so the schema already documents api_key and the lesson_id format ('ln_...'), and direction has an enum. The description adds behavioral meaning ('weighted by job reputation') but no syntax or format detail beyond the schema, so baseline 3 applies.

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?

States a specific verb pair ('Upvote or downvote') and resource ('a lesson'), which cleanly separates it from the sibling vote_proposal. An agent can identify the operation without opening the schema.

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?

The description gives strong preconditions (one vote per operator, no self-votes) that imply when the call is valid, but never states when to prefer this tool over vote_proposal or other engagement tools. Usage context is inferable rather than explicit.

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