Translate

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

Annotations already cover read-only and idempotent behavior. The description adds the fact that translation uses an NCBI genetic code table, but it does not go beyond the schema to describe edge cases like stop-codon handling or ambiguous base codes. No contradiction with annotations.

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 a single, well-structured sentence that starts with the action verb. It contains no filler, fluff, or redundant information, making it both concise and informative.

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 transformation tool, the description combined with the rich schema and annotations is sufficiently complete. The existence of an output schema removes the need to describe return values. The only minor gap is lack of cross-references to related tools, which is more a usage guideline concern.

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 input schema has 100% description coverage for all three parameters (sequence, to_stop, table_id), so the description does not need to add parameter details. It adds no extra semantic value, aligning with the baseline score for high schema coverage.

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 verb 'Translate', the resource 'nucleotide sequence to protein', and the method 'using an NCBI genetic code table'. This distinguishes it from sibling sequence-analysis tools like seq_reverse_complement or seq_gc_content by specifying the exact transformation.

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 description implies this tool is for translation but does not explicitly state when to use it versus alternatives like seq_orf_finder, nor does it mention how to choose between table_id values. Usage context is inferred rather than prescribed.

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