create_ml_project

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

With no annotations, the description carries full burden. It discloses the verification step and error behavior, and mentions how the table URI is built. However, it does not detail side effects like overwriting existing projects or the exact creation process beyond the verification.

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 extremely concise with two sentences and a parenthetical suggestion, front-loading the main action and prerequisites. Every sentence adds value without 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 9 parameters and no annotations, the description covers the core requirement (table in memory) and error handling. The output schema exists, so return values need not be explained. It lacks detail on potential conflicts or auto-run behavior, but is still adequate for a creation 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 description coverage is 100%, so baseline is 3. The description adds context about the table source and the URI construction, but does not elaborate on other parameters like target_variable or prediction_type. The added value is marginal.

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 AutoML pipeline automation project from a CAS table, specifying the source and prerequisites. It distinguishes from siblings like run_ml_project and promote_table_to_memory.

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 states the prerequisite (table must be loaded in global memory) and provides actionable guidance when the condition is not met (use promote_table_to_memory and list_source_tables). It implicitly differentiates from sibling tools, though it does not explicitly state when not to use.

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