sample_triangle

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

The description discloses the return structure (triangle with lower-right null, n_acc=10, n_dev=10, expected_totals) and mentions it's for parity checking. Since no annotations exist, it carries full burden and adequately describes behavior without side effects.

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 with a front-loaded main purpose, followed by structured return fields. Every sentence adds value without redundancy.

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?

The description fully covers the tool's purpose, output structure, and context (sample data for demos). With no parameters and an output schema available, it provides sufficient information for correct 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?

There are no parameters, so baseline is 4. The description doesn't need to add parameter meaning, and it doesn't attempt to.

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 returns a 'classic textbook 10×10 cumulative-paid triangle (Friedland-style)' and mentions use cases like demos, examples, and server verification. It distinguishes from sibling tools by being a sample dataset, while siblings like parse_csv_triangle handle real data.

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 explicitly states when to use the tool (demos, examples, verifying server is working) but doesn't explicitly mention when not to use it or suggest alternatives. However, the context of being a sample triangle implies it's not for real analysis.

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