add_row_index

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

No annotations are provided, so the description must disclose behavioral traits. It does not state whether the operation modifies the dataframe in place or returns a new one, whether it overwrites an existing column, or how the df_name parameter affects behavior. The word 'reset' implies overwriting but is not explicit.

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: three sentences including an example, with no redundant information. The main purpose 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?

The tool is simple and an output schema exists, reducing the need to describe return values. However, the missing df_name explanation and lack of behavioral details (e.g., in-place vs. return) leave gaps. For a mutation tool with no annotations, more context would be expected for a higher score.

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 compensate. The example implies that start sets the starting value and column_name names the column, but df_name is never mentioned. This is insufficient for the 3-parameter schema.

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 states a specific verb+resource: 'Add or reset a numeric row index as a column.' It clearly differentiates from other column operations (e.g., create_column, rename_columns) by focusing on row indexing. The example further clarifies expected usage.

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 gives a clear use case: 'Useful after filtering to create unique IDs or reset row numbering.' This tells the agent when to use the tool, though it does not explicitly name alternatives or exclusion criteria, preventing a 5.

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