evaluate_answers

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

With no annotations, the description carries the full burden. It discloses that the tool uses TF-IDF cosine similarity and requires no API key or LLM call, implying a lightweight read operation. However, it does not state whether the tool modifies any data, its idempotency, or potential error conditions.

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 very concise: two sentences that convey purpose, method, and key usage details. No extraneous words, and the information is front-loaded.

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?

For a tool with an output schema and no annotations, the description covers the core purpose and constraints. It lacks information about prerequisites (e.g., the dataset must exist and have golden answers) and error handling, but overall it is complete enough for an agent to use correctly.

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 description adds critical semantic information beyond the input schema: it explains the ordering constraint for actual_answers and the default behavior for the version parameter. While dataset_path lacks description in both schema and description, the provided hints for the other two parameters are valuable.

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 identifies the tool's purpose: scoring LLM/RAG answers against a golden dataset using TF-IDF cosine similarity. It specifies the method and notes that no LLM call or API key is needed, which distinguishes it from potential alternatives. The sibling tools are all dataset management actions, so the evaluation tool stands out.

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 specific usage instructions: the order constraint on actual_answers and the option to omit version for the current committed dataset. Though it doesn't explicitly compare to siblings, the context of siblings being non-evaluation tools makes the guidelines sufficient.

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