replace_values

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

It discloses one key behavior, 'Types are auto-cast,' which helps with type mismatches. However, it doesn't state whether the operation mutates the dataframe, how missing values are handled, or whether the replacement is exact/substring. With no annotations, these crucial behaviors remain undocumented.

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 two short sentences plus an example, with no fluff. It front-loads the core action and uses whitespace effectively.

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 simple value-replacement tool, it includes appropriate usage context, a concrete example, and auto-cast behavior. The output schema covers return values, so that absence isn't an issue. It would benefit from mentioning df_name and mutation behavior, but the overall package is solid.

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 example explains column, old_value, and new_value, but df_name is never mentioned. Since schema descriptions are absent, the description should cover all parameters. It covers three of four, leaving potential confusion about targeting a specific dataframe.

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 specific verb+resource: 'Replace a specific value with another in a column.' The example provides concrete usage, and the sentence 'Fix known data entry errors or standardize values' clarifies intent. This distinguishes it from nearby siblings like apply_mapping by focusing on single value replacement.

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?

Explicitly instructs 'Use after identifying issues with get_unique_values,' giving a clear precondition. It doesn't mention alternatives or when not to use it, but for a simple tool the guidance is sufficient.

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