create_dataset

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

With no annotations, the description carries the full burden. It discloses that write SQL is blocked, only SELECT queries are allowed, and dry_run validates without changes. However, it does not mention side effects, authorization needs, or the immediate usability of the created dataset.

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 concise and front-loaded, with a clear opening sentence followed by a contextual paragraph and then parameter descriptions. Every sentence adds value without filler.

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 is not required to explain return values. It covers purpose, usage, constraints, and all parameters. It could mention potential errors or prerequisites, but overall it is adequate for the tool's complexity.

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 0%, so the description must compensate. It provides clear, concise descriptions for all five parameters in the Args section, adding meaning beyond the schema's type and default values.

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 virtual dataset for charting, specifying the verb 'Create', the resource 'dataset', and its SQL-based nature. It differentiates from siblings by positioning itself as the main entry point from analytics to visualization.

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 advises to use list_databases to find database_id, mentions that write SQL is blocked, and describes the dry_run option. It implies when to use (after having a validated SQL query) but does not explicitly compare with siblings like create_chart or run_sql.

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