spraay_compute_text_inference

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

Adds cost and provider details beyond annotations. Annotations already indicate non-read-only and non-destructive, but description provides cost range and model count, which aids decision-making.

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?

Two sentences, front-loaded with the main action, no wasted words. Efficient and clear.

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?

Covers main purpose, model sources, and cost. Output schema exists so return details not needed. Could mention error handling or rate limits but not essential for this moderate-complexity 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 coverage is 100%, so baseline 3. Description adds context on cost variation by model but does not elaborate on parameter specifics beyond schema.

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?

Clearly states the tool runs LLM text inference, specifies model sources and cost range. Distinguishes from sibling tools like image generation, embeddings, etc.

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?

Implied use for text inference but lacks explicit guidance on when to use over similar tools like spraay_chat or spraay_bittensor_chat_completions. No exclusions or alternatives mentioned.

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