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GC Content

seq_gc_content
Read-onlyIdempotent

Calculate the GC content percentage (0-100) of a nucleotide sequence, supporting DNA, RNA, and IUPAC ambiguity codes.

Instructions

Compute GC content of a nucleotide sequence as a percentage (0-100).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sequenceYesNucleotide sequence (DNA or RNA; case-insensitive). IUPAC ambiguity codes (R, Y, S, W, K, M, B, D, H, V, N) are accepted where documented.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
lengthYesTotal sequence length.
gc_countYesNumber of G and C bases counted.
sequenceYesThe input nucleotide sequence (uppercased).
gc_percentYesGC content as a percentage (0-100). Ambiguous bases (N, R, Y, ...) are excluded from the calculation.
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the agent knows this is a safe read-only operation. The description adds the output range (0-100) but discloses no other behavioral traits, such as how ambiguous IUPAC codes are counted or error handling. This is adequate but not rich in added context beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence of 12 words. Every word is informative, with no redundancy or filler. It is an excellent example of conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple (one required parameter), has full schema coverage, a rich output schema, and comprehensive annotations. The description adequately covers the core function. It could optionally mention handling of ambiguous codes, but the schema already covers that, so the context is essentially complete. A 5 would require more nuance, e.g., return format or edge-case behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%: the 'sequence' parameter is fully described with type, minLength, and details about case-insensitivity and IUPAC codes. The tool description itself adds no parameter-level information, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Compute'), a clear resource ('nucleotide sequence'), and the exact output ('percentage (0-100)'). This fully distinguishes it from sibling tools like seq_motif_scan or seq_reverse_complement, leaving no ambiguity about the tool's function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The context is clear: use this tool when you need GC content as a percentage. No explicit exclusions or alternatives are mentioned, but the tool's purpose is so specific that usage is evident. However, it does not name alternative tools or state when not to use it, so it falls short of a 5.

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

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