look_and_say_sequence

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

No annotations are provided, so the description carries the full burden. It states the tool generates a sequence but doesn't disclose behavioral traits like output format (e.g., list of strings/numbers), handling of invalid inputs (e.g., non-numeric 'start'), performance characteristics, or error conditions. The description is functional but lacks operational details needed for safe invocation.

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 front-loaded with the core purpose in the first sentence. The domain/category tags are efficiently appended. There's no wasted text, though it could benefit from slightly more detail given the lack of annotations and schema descriptions.

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 no annotations, 0% schema description coverage, and no output schema, the description is incomplete. It doesn't compensate for missing structured data: parameters are unexplained, behavioral traits are omitted, and return values are unspecified. For a tool with two required parameters and no annotations, this leaves significant gaps for an AI agent.

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%, and the description doesn't explain the parameters 'start' (e.g., expected format, valid ranges) or 'terms' (e.g., meaning, constraints). It mentions 'starting from a number' but doesn't clarify if 'start' is a string or numeric input, or what 'terms' represents (e.g., number of sequence terms to generate). The description adds minimal value beyond the bare schema.

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 purpose: 'Generate the look-and-say sequence starting from a number.' It specifies the verb ('generate'), resource ('look-and-say sequence'), and starting condition. However, it doesn't explicitly differentiate from sibling tools like 'collatz_sequence' or 'fibonacci_sequence' beyond the domain/category tags, which are helpful but not explicit differentiation.

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 provides minimal guidance with only domain/category tags ('Domain: arithmetic, Category: iterative_sequences'). It doesn't specify when to use this tool versus alternatives (e.g., other sequence generators), mention prerequisites, or outline exclusions. The tags imply context but lack explicit usage instructions.

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