ask_ai

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the payment mechanism ('Costs are automatically deducted from your Solana wallet balance in USDC') and supported models ('Supports Claude and GPT-4 models'). However, it lacks information about rate limits, error handling, response format, or authentication requirements, which are important for a paid service tool.

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 perfectly concise with three sentences that each serve a distinct purpose: stating the core function, explaining the payment mechanism, listing supported models, and providing usage guidance. There's no wasted language, and the information is front-loaded with the most critical details first.

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 that this is a paid service tool with 4 parameters, no annotations, and no output schema, the description provides adequate but incomplete context. It covers the payment mechanism and supported models well, but lacks information about response format, error conditions, rate limits, or authentication requirements that would be important for proper tool invocation in a production 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?

The schema description coverage is 100%, providing complete parameter documentation in the structured fields. The description doesn't add any parameter-specific information beyond what's already in the schema, so it meets the baseline expectation. No additional semantic context is provided for the parameters beyond what the schema already covers.

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 specific action ('Send a prompt to an AI model'), identifies the resource ('via SolanaProx'), and distinguishes this tool from its siblings (check_balance, estimate_cost, list_models) by focusing on execution rather than querying or listing. It provides a complete purpose statement with both the primary function and the payment mechanism.

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 this tool ('Use this for any AI inference task'), which directly addresses its primary use case. However, it doesn't explicitly mention when NOT to use it or provide specific alternatives among the sibling tools (e.g., using estimate_cost first for cost estimation or list_models for model discovery), leaving some room for improvement in comparative guidance.

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