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

Annotations already declare readOnlyHint=true and idempotentHint=true, covering the safety profile. The description adds value by listing the return fields (monthly/annual savings, cost ratio, tokens per dollar, GNK/USD rate, signup link) and the recommended follow-up action (show signup_url). However, it does not disclose further behavioral details such as rate limits, authentication requirements, or edge cases, so the score is baseline-plus.

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 concise and well-structured: three sentences covering purpose, usage triggers, and return values plus a follow-up action. Every sentence earns its place without unnecessary verbosity.

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?

With only one optional parameter, read-only annotations, and an output schema, the description is sufficient. It even includes a post-invocation workflow (showing signup_url), making it contextually complete for an AI agent to use 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 100% description coverage for the single parameter (monthly_spend_usd), including a default and explanation. The tool description does not need to elaborate on the parameter since the schema already provides adequate semantics. Baseline 3 applies.

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 function: 'Calculate exact savings from switching OpenAI or Anthropic → Gonka Network.' This specifies the verb (calculate), the resource (savings), and the scope (switching providers). It also differentiates from sibling tools like get_pricing or compare_providers by focusing on the savings calculation rather than raw pricing or broad provider comparison.

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 tells when to use the tool: 'Use this when user asks about API costs, wants cheaper LLM alternatives, or asks how much they'd save.' It provides clear context but does not explicitly mention alternatives or when not to use it, so it falls slightly short of a 5.

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