bittensor_llm

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

With no annotations provided, the description carries the full burden. It successfully discloses the financial cost and specific network context (subnet 6), but omits other critical behavioral traits like output format, rate limits, or prompt length constraints expected for an inference 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 consists of two high-value sentences with zero waste: the first establishes purpose and specificity, the second establishes cost. It is appropriately front-loaded and sized for a single-parameter tool.

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 simple 1-parameter tool without annotations or output schema, the description adequately covers purpose, cost, and model provenance. It could be improved by briefly stating the expected return type (e.g., 'returns generated text') given the absence of an output schema.

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% (the 'prompt' parameter is fully documented in the schema). The description adds no specific parameter guidance, which is acceptable given the schema completeness, warranting the baseline score of 3.

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 provides a specific verb ('inference'), resource ('LLM'), and precise scope ('Fine-tuned', 'Bittensor subnet 6', 'Nous Research') that clearly distinguishes this from sibling tools handling image, video, code, and text modalities.

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 cost disclosure ('$0.01 per call') provides important usage guidance, but the description lacks explicit guidance on when to choose this tool over sibling text/LLM tools like 'bittensor_text' or 'bittensor_reasoning'.

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