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

The description adds valuable behavior beyond the annotations: joined emoji sequences count as one emoji, legal symbols are kept, and the output includes a removal count. It also states the source. No contradiction with readOnlyHint, idempotentHint, or destructiveHint exists.

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 compact and front-loaded: operation in the first clause, edge cases in the second sentence, and return value in the third. The source attribution is the only non-operational detail and it is brief.

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 one-parameter, non-destructive, idempotent text transform with an output schema, the description is complete: it defines behavior, preserves relevant edge cases, and announces both return elements. Nothing needed for correct invocation is missing.

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 single required parameter 'text' is already fully described in the input schema as 'The text to clean.' The description adds no extra semantic detail about the parameter itself, so the schema-coverage baseline of 3 is appropriate.

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 and resource: 'Remove emoji from a block of text, leaving the words and normal punctuation intact.' This clearly distinguishes it from sibling text utilities such as hopi_remove_line_breaks and hopi_remove_extra_spaces by naming the exact target and preservation behavior.

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 use case is clear: use this when the input is raw text containing emoji that should be stripped while preserving words and punctuation. It does not explicitly name alternatives or exclusions, but the focused emoji-specific behavior and sibling set make the intended context obvious.

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