tealflow_list_datasets

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds value by stating the output is a string with dataset names, descriptions, usage info, and relationships, plus the CDISC standards adherence. It also discloses that it lists 'standard ADaM datasets,' which is a scope limitation. This goes beyond the annotations without contradicting them.

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 Args, Returns, Includes, and Examples sections. It is longer than necessary but each section earns its place by clarifying output and usage. The redundancy in the 'Includes' list (which largely mirrors Returns) is minor, but the format aids scanning.

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 simplicity (one optional parameter, no required inputs) and the presence of an output schema, the description covers the essential aspects: purpose, parameter, return format, and content. It lacks error scenarios or specifics about dataset relationships, but for a read-only list tool, this is adequate. The main gap is sibling differentiation, which is already penalized in usage_guidelines.

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?

The schema for response_format is minimal (type and default), with 0% schema description coverage. The description fully compensates by explaining the parameter's purpose, valid values ('markdown' for human-readable, 'json' for machine-readable), and default behavior. This adds significant meaning beyond the 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 clearly states the tool's function: 'List available clinical trial datasets in the project.' It identifies a specific verb ('List'), a specific resource ('datasets'), and provides context (clinical trial, ADaM standards). This effectively distinguishes it from siblings like list_modules and get_module_details, which focus on modules rather than datasets.

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 examples but lacks explicit guidance on when to use this tool versus alternatives. It does not mention sibling tools like tealflow_discover_datasets or tealflow_get_dataset_info, nor does it state when not to use this tool. Usage context is implied ('available for use with Teal clinical modules'), but there is no direct differentiation or exclusion.

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