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

The description adds useful context beyond the annotations by mentioning the cost (~$0.03) and promising a 'concise answer'. However, it does not disclose other behavioral traits such as potential non-determinism, latency, or that the tool may consume credits. Annotations provide readOnlyHint=false and openWorldHint=true, but the description does not elaborate on these, so transparency is moderate.

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 and tight: one sentence covering purpose, result type, and cost. There is no filler or redundant information. It is front-loaded with the core purpose ('Ask Claude LLM any question') and efficiently communicates the key facts.

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 tool with a single string parameter and an output schema, the description is quite complete. It covers the tool's purpose, the nature of the response (concise), and a cost indicator. It does not explain return structure, but the output schema covers that. The only notable gap is the lack of context about when to use this over ask_ai_pro, but overall it provides sufficient context for a simple AI-query 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?

The schema already fully documents the only parameter 'q' with 'Your question', and the description adds no additional semantic detail beyond that. The phrase 'any question' simply reinforces the unrestricted nature of the input but does not introduce new constraints or format details, keeping this at baseline for high schema coverage.

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 function: 'Ask Claude LLM any question, concise answer.' This is a specific verb (ask) with a clear resource (Claude LLM) and outcome (concise answer), effectively distinguishing it from sibling tools like ai_image, ai_music, or ai_vision. The inclusion of cost (~$0.03) also provides practical context.

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 offers no explicit guidance on when to use this tool versus the sibling 'ask_ai_pro'. It says 'any question' but does not mention exclusions or alternatives, leaving the agent to guess whether ask_ai or ask_ai_pro is more appropriate for a given query. This lack of differentiation makes the usage guidance vague.

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