add-dataset-examples

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

No annotations are provided, so the description carries the full burden. It discloses that metadata will automatically include synthetic generation info via MCP. However, it does not mention error conditions, permissions, or other side effects beyond the metadata addition.

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 front-loaded with purpose, followed by usage guidelines, example, and return. It is relatively concise, though the example usage is somewhat vague. Could be slightly tighter but overall well-structured.

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 no output schema and 0% schema coverage, the description should be more complete. It mentions expected return but not details. It does not cover error handling, dataset existence, or validation beyond duplicate checking. Could be more thorough.

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 0%, so description must compensate. It explains that each example includes input, output, and metadata, but does not detail the expected structure of input/output objects beyond being objects. It adds some meaning but not comprehensive.

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 it adds examples to an existing dataset, with specific verb 'add', resource 'examples', and context. It distinguishes itself from sibling 'get-dataset-examples' by mentioning that tool for checking duplicates.

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 checking existing examples with 'get-dataset-examples' to avoid duplicates and follow patterns, and provides an example usage scenario. It lacks explicit when-not-to-use statements but gives clear context.

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