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

Annotations already declare read-only, idempotent, open-world, non-destructive behavior. The description adds important context beyond that: output switches to a decimal string past 15 digits, pricing is $0.001 USDC per successful call, and an HTTPS fallback endpoint is provided.

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 purpose and examples, followed by range, output format, pricing, and endpoint. It is mostly tight, though the pricing and HTTPS details add operational length that is justified for a paid API.

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 simple one-parameter sequence tool with no output schema, the description is complete enough: it explains the sequence, indexing range, example outputs, output formatting behavior, cost, and alternate endpoint. Annotations carry the safety profile.

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 0%, but the single parameter has built-in minimum and maximum constraints. The description compensates by explaining that n is the index into the sequence, restating the 0..64 range, and giving concrete example values that clarify indexing.

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 exactly what is computed: the nth large Schröder number, with illustrative values S(0)=1, S(1)=2, S(5)=394. The word 'large' distinguishes it from the sibling schroder_little, so an agent can tell the two apart without opening the schema.

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?

It gives the valid range n=0..64 and examples, which provides useful context for invocation. However, it does not explicitly say when to choose this over schroder_little or name alternatives, leaving that inference to the agent.

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