eval_generate_cases

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

With no annotations provided, the description carries full burden. It discloses that the tool calls an external generator, requires an API key, and returns a specific data structure. It does not mention potential latency or costs, but is transparent about the dependency on an underlying judge model and the generation process.

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 a clear one-sentence summary. It then follows a structured docstring format with Args and Returns sections. While somewhat lengthy, each sentence adds value; minor redundancy could be trimmed without losing clarity.

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 complexity (4 parameters, no schema descriptions, no annotations), the description covers purpose, parameters with defaults, output structure, and prerequisites (API key). It does not mention error handling or edge cases (e.g., API failure), but for a generation tool, the provided information is sufficient for an agent to use 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%, so the description must compensate. It provides a detailed 'Args' section explaining each parameter: 'from_text' (source text), 'n' (number of cases, default 10), 'task' (QA, summarization, hallucination with explanations), and 'judge_model' (provider:model string with default). This fully adds meaning beyond the 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 verb 'Generate' and the resource 'synthetic eval cases' from a source text. It distinguishes itself from sibling evaluation metrics by focusing on generation rather than evaluation, and provides specifics like producing 'input', 'expected_output', and 'context' for each case.

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 mentions eliminating the cold-start problem when building an eval suite, implying use when starting from scratch. It also notes the requirement of a provider API key. However, it does not explicitly state when not to use or provide alternatives among siblings, though the sibling tools are mostly evaluation metrics, making this tool's purpose distinct.

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