create_catalog

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

With no annotations, the description carries the behavioral burden. It discloses key side effects: creates fresh catalog with all ML schema tables, automatically connects, and optionally creates a name alias instead of numeric ID. It does not mention error cases or prerequisites, but for a creation tool it is reasonably 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 well-structured with a clear one-line summary, followed by Args, Returns, and Example sections. Each sentence serves a purpose, and the example adds practical clarity without unnecessary verbosity. It is appropriately sized for a tool with three parameters and a non-trivial return value.

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 still goes further by outlining the returned JSON fields. It also explains the auto-connect behavior, which is essential context. It lacks explicit failure-mode or permission information, but for a creation tool with a clear output schema, it is largely 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 coverage is 0%, so the description must compensate fully. It does: hostname is explained with examples, project_name is defined as the domain schema name, and catalog_alias gets a detailed explanation of its purpose and an example URL. All three parameters receive meaningful semantic enrichment beyond the bare schema.

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 opens with a specific verb+resource: 'Create a new DerivaML catalog with all ML schema tables.' It clearly defines the scope (fresh catalog with specific tables) and distinguishes from siblings like clone_catalog or create_catalog_alias by emphasizing the creation of a new catalog with ML schema and automatic connection.

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 gives clear context for when to use the tool—creating a new catalog—and explains the automatic connection behavior. However, it does not explicitly compare with alternatives like clone_catalog or connect_catalog, nor state when NOT to use this tool, so it stops short of full alternative guidance.

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