Get a Realia coin

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

Annotations already establish that the operation is read-only, non-destructive, and idempotent. The description adds the non-obvious behavioral nuance that the result exposes the underlying dataset's schema, which is central to the tool's purpose. This is useful context 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?

Two short sentences carry the full purpose, key identifiers, and return concept with no filler. The framing question is the only extra flourish and it helps an agent understand the tool's intent rather than adding noise.

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 one-parameter lookup with rich annotations and an output schema, the description covers the lookup key, scope, and core return component. It could include an explicit alternative-routing hint, but the low complexity makes the definition complete enough for correct invocation.

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 already documents the sole parameter at 100% coverage, so the description adds little new semantic weight. It restates that the value can be a mint address or a coin id, which matches the schema and provides no additional format or disambiguation details.

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 names a specific verb ('look up'), a single resource ('one coin'), and the accepted keys ('mint address or coin id'), then states the distinctive return value (the backing dataset and its schema). The framing question distinguishes it from sibling dataset tools by clarifying what question it answers.

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 clear context for use: when you have a coin identifier and want the coin along with the dataset behind it. It does not explicitly contrast with get_dataset or search_datasets, but the scope ('one coin' by its own id) makes the intended scenario unambiguous.

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