add-dataset-examples

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

With no annotations, the description partially discloses behavioral traits, such as automatically adding metadata indicating synthetic generation via MCP, and returning a confirmation. However, it does not mention side effects like appending vs. replacing, or limits on example count.

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 in three paragraphs, front-loads the purpose, and includes usage guidance and an example. It is concise but could be slightly trimmed without losing information.

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 low schema coverage, the description covers the operation, example structure, automatic metadata, and expected return. It references a sibling tool for pre-checks. Missing details like error handling or dataset existence validation, but sufficiently complete for its complexity.

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 description coverage is 0%, so the description must compensate. It mentions that examples contain input, output, and metadata but does not clarify the structure beyond the schema names. It lacks details on formats, constraints, or optional fields, adding minimal value over 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 it adds examples to an existing dataset, specifying each example includes input, output, and metadata. It also references a sibling tool (get-dataset-examples) for checking existing examples, distinguishing the tool's purpose.

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 instructs to use get-dataset-examples to check existing examples and avoid duplicates, and to follow existing patterns. This provides clear guidance on when and how to use the tool, though it does not explicitly state when not to use it.

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