clean_impute

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

No annotations are provided, so the description carries the full burden of disclosing side effects. It states the action (fill nulls) and strategies, but it does not disclose whether the operation mutates the source in place, returns a new dataset, or how it handles invalid column names or missing strategies. This leaves the agent uncertain about the tool's behavioral profile; the description does not contradict any annotations, but it is incomplete for a mutation tool.

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 the core action and then provides structured details on the mapping format. It is relatively concise, but the example spans multiple lines with awkward backtick placement, making the formatting slightly less clean. Overall, every sentence earns its place, but minor formatting quirks prevent a perfect 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?

Given that the tool has three parameters, a nested mapping object, and no annotations, the description does a good job explaining the essential input structure. It covers the mapping strategies and an example, so the agent can invoke it. However, it lacks explicit guidance on when to use this tool versus alternative cleaning operations and does not mention side effects or prerequisites, which are important for a transformation tool. The presence of an output schema reduces the need to describe return values, but the description still leaves some contextual gaps.

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 provides no descriptions (coverage 0%), so the description is the sole source of meaning. It excellently explains the `mapping` parameter with a type template, a list of strategies, and a concrete example. The `source_id` and `alias` parameters are not explicitly described, but their purpose is self-evident from names and the schema, and the complex mapping parameter is fully covered, justifying a high score.

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 opens with a clear verb-phrase 'Fill nulls in named columns with a strategy,' naming the resource (columns) and the action (impute missing values). It also distinguishes itself from sibling clean_* tools by focusing specifically on imputation, and it explicitly defines the mapping structure and available strategies.

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 clearly implies the tool is for imputing missing values, and the mapping format is detailed. However, it does not explicitly state when to prefer this over other cleaning tools (e.g., clean_replace) or provide any exclusions such as column type requirements. The context is clear but lacks direct alternatives/contrasts, so it earns a 4 rather than a 5.

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