bell_triangle

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

No annotations are provided, so the description must fully disclose behavioral traits. It states the tool generates Bell's triangle, implying a read-only computation, but does not mention performance characteristics (e.g., computational complexity for large n), error handling, or output format. For a tool with no annotations, this is insufficient behavioral context.

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 extremely concise: a single sentence that directly states the tool's function and includes domain/category metadata. There is no wasted verbiage, and the key information is front-loaded, making it efficient for an agent to parse.

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 computational nature, no annotations, no output schema, and low parameter schema coverage, the description is incomplete. It lacks details on output format (e.g., list of lists, matrix), error conditions, and performance limits. For a tool that generates combinatorial structures, more context is needed for reliable agent use.

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 has one parameter 'n' with 0% description coverage. The description adds that 'n' specifies the row limit ('up to row n'), which clarifies its meaning beyond the schema's type annotation. However, it does not detail constraints (e.g., n must be non-negative integer) or examples, leaving some ambiguity. With low schema coverage, this partial compensation warrants a baseline score.

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 Bell's triangle up to row n.' It specifies the verb ('Generate'), resource ('Bell's triangle'), and scope ('up to row n'). However, it does not explicitly differentiate from sibling tools like 'bell_number' or 'bell_sequence', which are related combinatorial tools, so it falls short of a perfect score.

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 no guidance on when to use this tool versus alternatives. It mentions the domain ('arithmetic') and category ('combinatorial_numbers'), but does not specify use cases, prerequisites, or comparisons to siblings like 'bell_number' or 'bell_sequence'. This leaves the agent without clear direction on tool selection.

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