recommend_tasks

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 heuristic-driven method, the inputs it scans (column types and shapes), and the detailed output structure (target candidate, predictor list, feasibility, challenges, first step). This is notably transparent.

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 two sentences, front-loaded with the core purpose, followed by a concise, structured breakdown of the heuristic approach and output components. No filler or redundancy.

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 single self-evident parameter and presence of an output schema, the description is largely sufficient, covering method, scope, and result format. It could mention prerequisites like whether the source must be loaded, but this is a minor gap.

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 has zero description coverage and the tool description does not explicitly explain the source_id parameter. Although the name is self-explanatory, the description should compensate for the lack of schema documentation, and it does not.

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 uses a specific verb ('Propose') and resource ('ML / analytical tasks the data is well-suited for'), and enumerates concrete task types (classification, regression, etc.), making it clearly distinct from sibling tools like suggest_plots or infer_recipes.

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 implies when to use the tool (when you want ML/analytical task suggestions based on data shape) but provides no explicit guidance on when not to use it or how it compares to alternatives such as infer_recipes or auto_explore.

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