ai_reply_suggestion

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

With no annotations, the description carries full burden. It transparently states that the AI reads full conversation context and suggests a personalized reply, and can be customized with a system prompt. This discloses key behavioral traits (read-only suggestion, AI-driven) without contradictions. However, it does not mention potential rate limits, authentication requirements, or side effects like logging, which would merit a 5.

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 extremely concise: two sentences with no wasted words. It is front-loaded with the primary purpose, and every sentence adds value (first sentence states action and resource, second explains context and customization). Perfectly structured for quick comprehension.

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 output schema exists, return values need no explanation. The description covers the main use case (suggestion generation), customization, and hints at AI behavior. It could optionally mention that the suggestion is based on full conversation, but that is already stated. Slightly above average because it is sufficient for an agent to correctly invoke the tool.

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 100%, meaning all parameters already have descriptions in the input schema. The tool description adds minimal extra meaning beyond reiterating the system_prompt parameter ('Customize with a system prompt'). As per guidelines, baseline is 3 when coverage is high, and the description does not significantly enhance understanding of the parameters.

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: 'Get an AI-generated reply suggestion for a conversation.' This uses a specific verb ('get') and resource ('AI-generated reply suggestion'), and distinguishes itself from sibling tools like reply_to_message (which sends a message) and generate_caption (which generates captions, not replies).

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 implies usage for generating suggestions before sending a reply, but it lacks explicit guidance on when to use this tool versus alternatives (e.g., reply_to_message for direct sending, generate_caption for different content). No when-not-to-use or conditional advice is provided.

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