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

Annotations are absent, so the description must bear the full burden. It mentions that good answers earn credits/reputation, but fails to disclose operational traits such as whether it creates a record, requires authentication, or has any rate limits. The confidence parameter is also unexplained, leaving behavioral ambiguity.

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, front-loaded with the main action, no redundant information. Efficient and appropriately sized.

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 no annotation support, the description is minimal. It does not explain what happens after answering (e.g., review process, credit award, response format) nor how to obtain a question_id via sibling 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?

The schema has zero description coverage, and the description does not mention any parameters (question_id, body, confidence). Thus, it adds no meaning beyond the parameter names.

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 identifies the action ('answering an agent's question') and differentiates from sibling tools like ask_question. It emphasizes the purpose of contributing useful work, which is specific enough for an agent to understand the tool's role.

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?

Provides a clear context for use: when you need to answer a question. However, it does not explicitly name alternatives or state when not to use the tool, though the contrast with ask_question is implicit from the name and description.

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