create_data_asset

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: the tool returns confirmation of creation request validity (not success), creation takes time, and monitoring requires 'wait_until_ready()'. It also explains that data assets are versioned and immutable, and distinguishes between internal and external storage. However, it lacks details on permissions, rate limits, or error handling, which are important for a creation tool.

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 well-structured and appropriately sized, with key information front-loaded: it starts with the core purpose, then explains data asset properties, source types, and behavioral notes. Each sentence adds value, such as explaining versioning, storage types, and monitoring. It could be slightly more concise by avoiding minor redundancy, but overall it is efficient and clear.

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 of the tool (with multiple source types and a detailed input schema) and the presence of an output schema, the description is reasonably complete. It covers the purpose, key behaviors, and usage context, and the output schema likely handles return values. However, without annotations, it could benefit from more details on permissions or error scenarios to fully guide an agent in a production environment.

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 schema description coverage is 0%, so the description must compensate. It mentions sources like S3 buckets, computation results, and combined assets, which map to the 'source' parameter and 'data_asset_ids', and notes that data assets have names, tags, and mounts. However, it does not detail all parameters (e.g., 'custom_metadata', 'target', 'results_info') or their specific semantics, leaving gaps despite the low schema coverage.

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 creates a new data asset from various sources (S3, GCP, computation results, combined assets), specifies that data assets are versioned, immutable collections for computational workflows, and distinguishes between internal and external storage types. It provides a specific verb ('create') with detailed resource context, making it distinct from sibling tools like 'get_data_asset' or 'search_data_assets'.

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 explicitly mentions when to use this tool: for creating data assets from AWS S3, GCP Cloud Storage, computation results, or combining existing data assets. It also provides guidance on monitoring creation progress with 'wait_until_ready()'. However, it does not explicitly state when NOT to use it or compare it to alternatives like 'attach_data_assets' or 'update_metadata', which could help differentiate use cases.

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