create_ml_project

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

With no annotations, the description carries the full burden. It adds useful behavioral context: SAS auto-detects the target's measurement level, and the data table must be loaded in CAS. However, it does not disclose side effects such as the automatic pipeline run behavior (auto_run), potential failure modes, or permission requirements.

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?

Three concise sentences, front-loaded with the action and resource. Every sentence adds meaningful context, with 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 presence of an output schema, the description need not explain return values. It covers the key prerequisite (data in CAS) and important behavioral nuance (auto-detection). It doesn't mention the auto_run default, but that detail is available in the schema, making it adequately complete.

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 the baseline is 3. The description adds value by explaining how SAS auto-detects target measurement level and clarifying that target_event_level applies to binary targets only, which complements the schema's parameter descriptions.

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 starts with a specific action ('Create') and a specific resource ('AutoML pipeline automation project'), clearly distinguishing it from sibling tools like list_ml_projects, run_ml_project, and delete_ml_project. The scope is immediately evident.

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 clearly indicates the tool's purpose—creating a new project—which makes the primary use case obvious. However, it does not explicitly compare against alternatives or state when not to use it, falling short of full explicit guidance.

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