list_datasets

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

No annotations are provided, so the description carries full burden for behavioral disclosure. It only states 'List all datasets' and does not mention safety (e.g., read-only), required permissions, or potential side effects. This is a significant gap for a tool that interacts with data.

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 two sentences long with no wasted words. It efficiently states the functionality and usage context, making it easy for an AI agent to parse quickly.

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?

While the description covers the tool's purpose and usage context reasonably well, it lacks details about the output format (e.g., list of dataset names, paths). Given there is no output schema, this missing information reduces completeness for an agent that needs to use the results.

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 input schema already provides 100% coverage for the single parameter 'filter' with full description (case-insensitive keyword). The description adds minimal value beyond calling it 'name keyword,' which is redundant. Therefore, baseline score of 3 is appropriate.

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 'List all datasets in Observe, optionally filtered by name keyword' clearly specifies the verb (List) and resource (datasets). It also provides use context 'Use this to discover dataset paths before running queries,' effectively distinguishing it from sibling tools like get_dataset_schema (schema of specific dataset) and inspect_dataset (details of dataset) which operate on known 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 explicitly advises using this tool to discover dataset paths before running queries, providing clear context for when to use it. However, it does not explicitly mention when not to use it or list alternatives, leaving some ambiguity for edge cases.

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