remove_punctuation_marks

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

No annotations are provided, so the description carries the full burden. It thoroughly discloses character-level behavior, listing exactly what punctuation is removed (periods, commas, brackets, etc.) and what is preserved (letters, numbers, spaces, currency symbols, math operators). It does not mention file-level effects like whether the original file is modified, but the character behavior is highly transparent.

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 multiple sentences but every sentence adds value: it defines the action, provides the character sets, and gives use cases. No filler or repetition; it is well-structured and appropriately sized for the tool's complexity.

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 text-processing tool with an output schema, it covers the core behavior and use cases thoroughly. It lacks explicit parameter explanations and file-handling details, but given the low complexity and the existence of an output schema, these are not critical 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?

Schema description coverage is 0%, and the description does not explain any parameters (file_token, _range, worksheet). It adds no meaning beyond the schema's property names, forcing the agent to infer the purpose of file_token and optional scope parameters from context alone.

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?

Clearly states it removes punctuation marks from text cells while preserving alphanumeric characters and other symbols. This distinguishes it from siblings like remove_symbols or remove_numeric_characters by explicitly defining what is removed (punctuation) and what is preserved (symbols).

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?

Provides explicit use cases: 'useful for text preprocessing in NLP, removing sentence boundaries for analysis, cleaning text for word counting, or preparing data for text mining.' It implies when to use this tool but does not directly compare with alternatives or state when not to use it, so it lacks explicit exclusions.

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