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 burden. It discloses that metadata will automatically include information indicating synthetic generation via MCP. It also mentions the expected return. However, it does not discuss side effects, authorization needs, or what happens if the dataset does not exist. For a mutation tool, more transparency would be better, but the description provides reasonable insight.

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 paragraphs plus an example usage and expected return. It is well-structured, front-loading the main purpose, and each sentence provides relevant information. It is concise without being overly terse, earning a 4.

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 2 required parameters, no output schema, and no annotations, the description provides a reasonable overview: what it does, how to use it (check existing examples), and expected confirmation. However, it does not cover error conditions, validation rules, or whether the dataset must exist. It is adequate but not fully comprehensive for a mutation tool with moderate 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?

The input schema has 0% description coverage, so the description must compensate. It explains that each example includes input, output, and metadata, and that metadata will be automatically augmented. However, it does not describe the structure of the input/output objects or clarify if the user provides metadata or it is fully automatic. This adds some meaning but leaves gaps, resulting in a score of 3.

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 function: 'Add examples to an existing dataset,' specifying the verb (add), resource (dataset examples), and scope. It also details that each example includes input, output, and metadata, and that metadata will automatically include synthetic generation info. This clearly distinguishes from sibling tools like 'get-dataset-examples'.

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 explicit guidance: 'Before calling this tool, check existing examples using the get-dataset-examples tool to ensure you are not adding duplicate examples and following existing patterns.' This indicates when to use and a prerequisite action. It also gives an example usage. However, it does not explicitly state when not to use or alternatives, hence 4.

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