fill_missing

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

Annotations are absent, so the description carries the burden of behavioral disclosure. It explains what each strategy does and notes that columns=None fills all columns with nulls. However, it does not disclose whether the operation modifies the dataframe in place or returns a new one, nor does it mention any side effects or dependencies like the 'df_name' parameter.

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 concise and well-structured: it starts with the core purpose, lists strategies, provides parameter clarification, gives usage guidance, and includes a clarifying example. No sentence is wasted.

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?

While a separate output schema exists and return values need not be described, the omission of the 'df_name' parameter is a critical gap. Given there are 4 parameters and the description covers only two meaningfully, the tool lacks the contextual completeness needed for an agent to invoke it correctly, especially to identify which dataframe to mutate.

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 schema provides 0% description coverage, so the description must compensate. It explains 'columns' (None = all columns with nulls) and enumerates valid 'strategy' values, but it only indirectly implies the 'value' parameter ('value' (literal)) and completely omits 'df_name'. The example demonstrates usage of 'columns' and 'strategy', but not the other parameters.

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 'Fill missing values' and enumerates the supported strategies ('value', 'mean', 'median', 'mode', 'ffill', 'bfill'). This is specific and distinguishes it from the sibling tool 'drop_missing', which removes missing values instead of filling them.

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 on strategy selection: 'median' for skewed numeric, 'mean' for normal, 'mode' for categorical, and 'ffill'/'bfill' for time-series. However, it does not mention when to prefer filling over dropping missing values (e.g., versus 'drop_missing'), so there is no exclusion criteria.

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