transform_text

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

With no annotations, the description is the only behavioral disclosure. It merely says 'apply modules to text' without mentioning side effects, purity, error behavior, or output characteristics. This is insufficient for an agent to predict the tool's runtime behavior.

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 one concise, well-constructed sentence that front-loads the action. There is no filler or redundancy; every word earns its place.

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 tool with two parameters, no annotations, and no parameter descriptions, the description is far too sparse. It leaves ambiguity about how modules are specified, what the transformation entails, and how to interpret the result. The existence of an output schema doesn't compensate for the missing context in the description itself.

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 for the parameter meaning. It adds the acronym 'STM' and implies 'modules' are transformations, but it doesn't explain valid module values, the effect of the default null, or the structure of 'text'. The meaning is only barely extended beyond the 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 uses a specific verb ('apply') and identifies the resource ('text') and the mechanism ('semantic transformation modules'), which distinguishes it from sibling text tools like parseltongue_encode. However, it doesn't explain what STM are or what the result looks like, so it's clear but not fully refined.

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?

There is no guidance on when to use this tool versus alternatives such as single_chat or parseltongue_encode. The description only states what the tool does, providing no exclusions or contextual triggers.

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