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

With no annotations, the description carries full burden. It discloses automatic profiling, background training, fallback model, and polling mechanism. However, it does not state whether registration is idempotent, what happens on duplicate names, or any destructive side effects. Some behavioral aspects remain implicit.

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?

Two concise sentences front-load the purpose and then detail behavior. No unnecessary words or repetition. Every sentence adds 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 tool's moderate complexity (6 params, nested objects, no output schema), the description covers registration, input options, training behavior, and polling. It lacks explicit return value format and error conditions, but these are partly addressed by the schema and the polling hint.

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 100%, but the description adds meaningful context beyond parameter names and schema descriptions. It explains the three data input modes, the auto-suggestion for 'domain_aliases', and the role of 'async_train'. This framing helps an agent choose parameters wisely.

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 verb ('register') and resource ('dataset for semantic querying'). It enumerates input methods (columns, records, CSV) but does not explicitly distinguish from siblings like 'connect_data' or 'ingest_data'. However, the mention of background training and fallback model hints at its unique role.

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 provides clear context: use this tool to enable semantic queries on data, with immediate fallback and eventual accuracy improvement. It directs to 'list_datasets' for status polling. However, it does not specify when to avoid this tool (e.g., for external data sources) or compare with alternatives.

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