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

Annotations already declare read-only, idempotent, open-world, and non-destructive behavior. The description adds useful context: output format, an illustrative first-10 sequence, the valid n range, x402 pricing per successful call, and an HTTPS alternative.

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 purpose, then adds example output, valid range, price, and endpoint. Each sentence contributes useful context 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 one required parameter, rich annotations, and no output schema, the description covers what the tool returns, the valid range, example values, cost, and transport endpoint. It is complete enough for an agent to invoke it 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?

Schema description coverage is 0%, but the description compensates by explaining that n selects the prefix Q(1) through Q(n) and that n runs from 1 to 4096. This gives clear meaning beyond the bare integer schema, though it could be slightly richer on output adjacency.

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 resource and output: the first n Hofstadter Q terms as comma-separated integers, with an example and range. It is clear what the tool returns, but it does not explicitly distinguish itself from siblings such as hof_q or hof_q_sum.

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?

There is no explicit guidance on when to use this tool versus alternatives, nor any exclusions or sibling references. The description implies it is for retrieving a prefix of Q terms, but leaves usage selection entirely to inference.

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