detect_text_encoding

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

With no annotations provided, the description must fully disclose behavior. It only states the basic function without mentioning supported encodings, accuracy, constraints, or error handling (e.g., what happens if the text is empty or binary).

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 short and to the point, but it omits important details. It is not overly verbose, but the lack of structure (e.g., no separate sections for usage, output, or limitations) makes it less effective.

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?

Given that the tool has an output schema but no description explains what the detection result contains (e.g., encoding name, confidence score), the description is incomplete. For a simple tool, more context about return values is expected.

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 should compensate. It only repeats the parameter name and a trivial description ('Text to detect encoding of') without adding constraints, examples, or format requirements.

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 the action ('Detect') and the resource ('character encoding of a text string'). It is a specific verb+resource combination that distinguishes it from sibling tools like detect_language or analyze_tone.

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 other detection tools or encoding-specific utilities. No context about prerequisites or edge cases is provided.

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