create_execution_dataset

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 discloses the core behavioral traits: creates a dataset, links it to the execution for provenance, and returns a JSON with dataset_rid and execution_rid. It does not mention permissions or failure modes, but for a create operation, the essential side effect (creating a linked dataset) is stated.

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 structured with an opening statement, a usage hint, and an Args/Returns breakdown. It is easy to scan and contains no fluff. It could be slightly more compact, but the structure supports 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 tool's simplicity (2 params, no nested objects, output schema described in Returns), the description covers all necessary aspects: purpose, usage, parameters, and return value. It does not need to describe return fields in detail because the Returns line lists them. Annotations are absent, but the description is sufficiently complete for this tool.

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 coverage is 0%, so the description must compensate. It does: 'description: What this dataset contains' and 'dataset_types: Type labels (e.g., ["Training", "Augmented"])'. This adds meaning beyond the bare titles and defaults, explaining the purpose and format of each parameter.

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 opens with a specific verb+resource: 'Create a new dataset as output from this execution.' It clearly distinguishes from sibling tools like create_dataset (which may not be execution-linked) and create_execution (which creates executions). The phrase 'linked to this execution for provenance' adds a distinctive scope.

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 gives explicit when-to-use guidance: 'Use this when your workflow produces a new curated collection of data (e.g., augmented training data, filtered results).' It does not explicitly mention alternatives or when NOT to use it, but the given context is clear and helps the agent decide.

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