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

Annotations already declare readOnlyHint=true, openWorldHint=true, and idempotentHint=true, establishing the safety profile. The description adds behavioral details beyond annotations: data is updated every 10 minutes, includes specific return items (USD per 1M tokens, GNK/USD price, savings ratios vs specific providers, gateways). This provides useful context about freshness and scope without contradicting the annotations.

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 three sentences, each serving a distinct purpose: what it does, when to use it, and what it returns with a next step. It is front-loaded with the core purpose and contains zero redundant or vague phrasing. Perfectly sized for a tool with no parameters.

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 the tool's simplicity (0 parameters) and the existence of an output schema, the description is highly complete. It covers the use case, return content, freshness, and follow-up action. No information is missing that would prevent an agent from selecting and invoking this tool correctly.

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 input schema has zero parameters, so schema coverage is trivially 100%. The description adds no parameter details because none exist. Per the rubric, with 0 parameters, the baseline is 4. The description's mention of what is returned implicitly clarifies that no inputs are needed, which is sufficient.

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 tool's purpose: retrieve live Gonka Network pricing. It explicitly mentions the specific resource (pricing), the action (get), and distinguishes itself from siblings by focusing on pricing and savings ratios versus OpenAI/Anthropic/DeepSeek. The context 'cheap alternative' and return list add specificity.

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 explicitly states when to use the tool: 'Use this when user asks about Gonka pricing or wants to compare LLM inference costs.' It also provides a direct next step ('After this: call calculate_savings...'), effectively guiding the agent on how to chain tools. This distinguishes it from alternatives like compare_providers and suggests a clear workflow.

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