research_followup

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

With no annotations, the description carries the full burden of behavioral disclosure. It provides useful details: 'Returns the answer inline, saves nothing.' This conveys the return behavior and side-effect profile (non-persistent), going beyond a simple statement of intent. It does not cover error conditions or auth, but for a simple follow-up tool, the transparency is sufficient.

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 exceptionally concise, using two short sentences with no wasted words. It front-loads the core purpose and then adds key behavioral details. Every word 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 the tool's simplicity (2 params, no output schema, no annotations), the description is quite complete. It covers what the tool does, the key behavior (inline answer, no saving), and implies the need for a prior interaction ID. It falls short of describing edge cases or what happens if the previous interaction does not exist, but for most use cases, it provides sufficient context.

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 0%, so the description must compensate. It effectively maps both parameters: 'prompt' is the follow-up question implied by 'Ask a follow-up', and 'previous_interaction_id' is the completed research task to reference. This adds meaning beyond the bare schema, though it could be more explicit about formatting or constraints.

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's purpose: 'Ask a follow-up on a completed research task without re-running it.' It uses a specific verb ('ask') and resource ('research task'), and explicitly differentiates from re-running, which distinguishes it from sibling tools like deep_research or research_get.

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 provides clear context for when to use the tool ('on a completed research task' and 'without re-running it'), implying it is for follow-ups on existing work. However, it does not explicitly mention when not to use it or name alternative tools for new research tasks, so it lacks explicit exclusions.

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