load_data_source

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

No annotations are provided, so the description must fully disclose behavior. It explains synchronous and asynchronous execution and required keys for each source type. However, it lacks details on error handling, data size limits, or what happens if a source is missing.

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 with bullet points for source types and numbered guidelines. It is front-loaded with the core purpose. While somewhat lengthy, every sentence contributes valuable context.

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 no output schema, the description explains the return values (data_handle or job_id) and covers main use cases. It addresses sync/async and source-specific details, making it fairly complete for a data loading 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 100%, but the description adds substantial meaning beyond the schema. It details the required keys for each config type (pandas, file, sql, url), which the schema only mentions as a generic object. This is critical for correct parameter construction.

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 loads data from various sources into a data handle for forecasting. It lists specific source types (pandas, file, sql, url) with details, distinguishing it from sibling tools like save_data or fit.

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 provides explicit GUIDELINES on when to specify target_column, time_column, and how to handle date columns. It also explains synchronous vs async modes. However, it does not mention when not to use this tool or suggest alternatives like list_handles for already loaded data.

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