list_datasets

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

No annotations are provided, so the description bears the burden. It discloses the default limit of 20 and the filtering capabilities, but lacks details on pagination, sorting, or output structure. For a read-only list tool, this is acceptable but not rich in behavioral disclosure.

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 one-sentence purpose followed by a clear Args list. It is detailed but not verbose, with every line providing useful information. The front-loaded purpose makes it easy to scan.

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 has 6 optional parameters, no output schema, and is a straightforward listing operation, the description is fairly complete. It covers the default behavior, filtering options, and limit, which is sufficient for an agent to invoke it correctly, though it could mention pagination if applicable.

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 already covers all parameter descriptions (100% coverage), but the description adds meaningful detail beyond the schema: 'dataset_ids' as JSON array string, 'dataset_name' as exact match, 'dataset_name_contains' as substring, and an example for 'data_type'. This enriches understanding of how to use the parameters.

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 'Fetch LangSmith datasets' and explains the default behavior of returning all datasets when no arguments are provided. This distinguishes it from sibling tools like list_prompts or list_experiments, though it doesn't explicitly name alternatives.

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 context about when no arguments returns all datasets, implying a listing use case. However, it does not explicitly state when to prefer this tool over siblings like read_dataset or list_examples, nor does it mention any exclusions or prerequisites.

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