spraay_chat

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

Annotations already indicate readOnlyHint=false and openWorldHint=true. The description adds cost information ($0.005 USDC) and states it returns a completion, providing utility beyond annotations. No contradictions found.

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 two sentences long, front-loaded with the core action, and contains no redundant information. Every word adds value.

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?

The description covers the tool's purpose, required prerequisite (model discovery), and cost. An output schema exists, so return value explanation is unnecessary. It omits potential error scenarios or prerequisites like wallet balance, but overall is sufficiently complete for a straightforward chat 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?

With 100% schema description coverage, the baseline is 3. The description adds context about model format, discovery tool, and pricing, going beyond the schema's parameter descriptions, earning a higher score.

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 action ('Send a message to 200+ AI models via Spraay x402 Gateway') and mentions it returns a completion. It references spraay_models for discovery, distinguishing the lookup tool, but does not explicitly differentiate from other AI chat tools like spraay_bittensor_chat_completions or spraay_compute_text_inference.

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 advises to use spraay_models to discover model IDs, providing some pre-usage guidance. However, it lacks explicit when-to-use or when-not-to-use directives relative to sibling tools, leaving the agent to infer appropriate context.

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