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

The annotations already declare read-only and idempotent behavior, and the description adds that the result includes a fit verdict, column mapping, and missing-input identification. However, phrases like 'the places it delivers' are vague, and there is no disclosure of how the fit verdict is reached or what happens with invalid inputs.

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 short and front-loads the core question before listing outputs. The telegraphic output list and the unclear 'places it delivers' prevent a perfect structure score.

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?

With no output schema and three under-described parameters, the description supplies the gist and output categories but lacks clear return-value details and parameter definitions. An agent could probably call it successfully, but not with complete confidence in what it will get back.

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 define the parameters, but it never names objective, tool_name, or dataset_ref directly. It loosely hints at 'library tool' and 'THIS dataset/THIS question', but leaves objective unexplained and the parameter mapping implicit.

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 communicates that the tool evaluates whether a library tool fits a specific dataset and question, and it names concrete outputs (column mapping, missing required inputs, method-fit verdict). It is more informative than the tool name alone, though it does not explicitly contrast itself with sibling tools such as discover_tools or tools_schema.

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?

'Before naming a library tool' is an explicit workflow cue, and 'THIS dataset for THIS question' scopes when the tool applies. It gives no when-not-to-use guidance or named alternatives, so it does not fully reach explicit routing.

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