answer_question

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

With no annotations, the description carries the transparency burden. It discloses a 'clean abstention' on insufficient context, and explains that citations can be verified with get_evidence, giving agents a clear behavioral model. However, it does not explicitly state whether the operation is read-only or describe other failure modes.

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 description is four sentences, front-loaded with the primary purpose, and each sentence adds meaningful detail about verification, downstream integration, or abstention. No unnecessary words.

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 simple Q&A tool with an output schema, the description covers key workflow aspects: grounding, citations, verification, and abstention. It does not mention prerequisites or alternatives explicitly, but given the tool's low complexity, it is sufficiently complete.

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 only parameter is 'question' with a string type. The description fails to elaborate on question format, length, or examples, adding no meaning beyond the schema's type and name. Since schema coverage is 0%, the description should have compensated but does not.

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 opens with 'Answer a question grounded in the knowledge graph, with citations,' which clearly specifies the action, resource, and output. It distinguishes itself from sibling tools like query_knowledge by emphasizing citations and the downstream use of returned concepts.

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 indicates the tool is for getting cited answers, with chunk ids verifiable via get_evidence, and returned concepts feed write tools for hypotheses and decisions. It implies when to use it but does not explicitly exclude alternatives or name tools to prefer in other scenarios.

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