infer

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

With no annotations provided, the description carries the burden of behavioral disclosure and does a solid job: it reveals that every call is metered and paid per token, that a funded channel key is required, and that the response includes both the completion and a receipt with charges, routed model, and remaining balance. It could add failure behavior for insufficient balance or invalid model, but the key traits are transparent.

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?

Three dense sentences with no filler: the first sentence identifies the action, the second summarizes the return payload, and the third states the prerequisite. Everything essential is front-loaded.

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 paid inference call with complete parameter schema, the description covers the payment model, auth requirement, and return envelope. Even without an output schema, an agent knows what to expect and what it needs to call 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%, so the schema already documents all six parameters. The description adds useful context about auth via apiKey or BSVKEY_API_KEY and the receipt contents, but it does not need to compensate for schema gaps, hence the baseline 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?

States a concrete action, 'Run one metered inference', with a clear resource ('OpenAI-compatible' completion) and payment mechanism (per token in BSV from prepaid channel). This clearly distinguishes it from siblings like list_models, channel_balance, and open_channel, which serve different functions.

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 clearly establishes the context: use this when you want to perform an inference that will consume prepaid BSV credits, and it names the prerequisite of a funded channel key. It does not explicitly name alternatives or when-not-to-use cases, but the sibling names make the decision boundary reasonably clear.

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