evaluate_on_synthetic

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It mentions 'deterministic' but does not state read-only status, side effects, permissions, or failure modes. The tool could be a read operation, but this is not confirmed.

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?

Single sentence that front-loads the core purpose and a key constraint (no real data). Every word contributes value; no wasted space.

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 a simple input schema (1 parameter) and an output schema (implied by context), the description adequately conveys purpose and usage context. It could add behavioral details, but the presence of an output schema excuses the need to explain return values.

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 0% description coverage, and the description does not explain the sole parameter 'project_id' beyond its name. The agent cannot infer what the project ID represents or how it affects the evaluation.

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?

Description clearly states the tool runs a deterministic eval over synthetic data to verify the pipeline, distinguishing it from real-data evaluations. The verb 'run' and resource 'eval over synthetic data' are specific and actionable.

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 phrase 'no real data required' implies use when real data is unavailable or inappropriate, providing clear context. However, it does not explicitly mention alternatives like evaluate_on_validation_set or when not to use this tool.

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