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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It clearly states this is a read-only operation ('Reads on-chain state'), explains what the data represents (hourly averages committed on-chain), and explicitly discloses the limitation that it does not prove temperatures stayed within limits at every moment. It also notes network support (preprod and mainnet). It doesn't specify output format or error behavior, but the key behavioral caveat about hourly averages is well disclosed.

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 front-loaded with the core function ('Verify ... DIRECTLY from the Midnight blockchain'), followed by the key trust differentiator, the critical limitation, and network support. Every sentence earns its place: the first sentence states the purpose, the second explains why this tool exists (vs the dashboard), the third warns about what the data doesn't prove, and the fourth notes supported networks. No fluff or redundancy.

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 moderate complexity (4 optional parameters, no required parameters, no output schema), the description covers the essential context: what it verifies, why it's different from the dashboard, the hourly-average limitation, and supported networks. It doesn't describe the return format or error cases, but for a read-only verification tool with heavily documented parameters, the described context is largely sufficient. The trust/verification framing is particularly valuable for agent decision-making.

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 all four parameters are already described in the input schema. The tool description adds context about what the parameters filter (network, freezerId, hourStartUtc, contractAddress) by explaining the purpose of verification, but it doesn't add new parameter-level semantics beyond the schema. The baseline of 3 is appropriate because the schema does the heavy lifting and the description reinforces the verification context.

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 states a specific verb ('Verify'), a specific resource (GYOTAK cold-chain storage temperatures from the Midnight blockchain), and immediately distinguishes itself from the dashboard reading from GYOTAK's D1 database. It clearly explains the independent verification purpose and the hourly-average limitation, which differentiates it from sibling tools like verify_catch or verify_komon. The scope is precise: reads on-chain state and returns decoded hourly temperature records per freezer.

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 gives explicit guidance: use this when you need independent verification without trusting GYOTAK, unlike the dashboard. It also clarifies the limitation that hourly averages do not prove continuous compliance, which is a key usage consideration. It names the alternative context (the dashboard) and explains the trust difference, giving clear when-to-use reasoning.

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