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KASP / ARMS Allele-Specific Primers

kasp_primer_design
Read-onlyIdempotent

Design KASP/ARMS allele-specific genotyping primers for a SNP: two allele-specific forward primers differing only at the 3' terminal base (one per allele), each with the standard KASP universal tail (FAM for allele A, HEX for allele B), a deliberate internal ARMS secondary mismatch near the 3' end whose strength complements that primer's own natural allele mismatch (strong↔weak), and one common downstream reverse primer sized to a chosen amplicon range. Because a forward primer reads the antisense strand, each primer's 3' base sits opposite the complement of the other allele, so the two primers get different mismatch classes and are reported separately (graded from the measured PCR yields in Kwok et al. 1990). Reuses the site's nearest-neighbor Tm engine.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetYesNucleotide sequence (raw or FASTA; IUPAC accepted).
alleleAYesFirst allele (single base) — gets the FAM tail.
alleleBYesSecond allele (single base) — gets the HEX tail.
maxAmpliconNoMaximum amplicon length for the common reverse primer.
minAmpliconNoMinimum amplicon length for the common reverse primer.
snpPositionYes1-based position of the SNP on the forward strand. Must be 18 or greater: the allele-specific primers end on the SNP, so they need at least 17 bp of upstream template to build a core from.
targetCoreTmNoTarget Tm (°C) for the allele-specific primer core (before the universal tail).
addSecondaryMismatchNoEngineer the internal ARMS destabilising mismatch near the 3' end.

TDQS

A4/5.0
Behavior4/5

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

Annotations already provide readOnlyHint and idempotentHint, so the bar is lowered. The description adds valuable behavioral context: the antisense-strand interpretation, mismatch-class differences between the two primers, grading by Kwok et al. 1990 yields, and reuse of the Tm engine—none of which is in annotations.

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

Conciseness3/5

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

The description is a single dense run-on sentence that front-loads nearly all details without structural breaks. It is information-dense and not wasteful, but its lack of paragraphing or bullet-like separation makes it harder to parse quickly.

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?

For a complex 8-parameter design tool with no output schema, the description covers the core algorithm, key design constraints, and reporting distinction without explaining exact return values. It is substantially complete for an experienced user, though an explicit output description would have been useful.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description enriches parameter understanding by explaining why FAM/HEX tails are assigned per allele, how amplicon range relates to the reverse primer, and how the secondary mismatch interacts with the natural allele mismatch—value beyond the schema's property descriptions.

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 uses a specific verb ('Design') and resource ('KASP/ARMS allele-specific genotyping primers for a SNP'), and details the two allele-specific forward primers plus common reverse primer. This clearly distinguishes it from generic primer_design or aso_design siblings.

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

Usage Guidelines3/5

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

The description implies usage for SNP genotyping and KASP/ARMS applications, but it does not explicitly state when to prefer this tool over sibling tools like primer_design or aso_design, nor does it mention exclusions. Context is present but not fully actionable.

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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TDQS

A3.6/5.0
Disambiguation4/5

Most tools have highly specific purposes (e.g., crispr_grna_design vs base_editing_design vs prime_editing_design). However, there is some overlap in sequence analysis tools (characterize_sequence, sequence_report) and plasmid annotation tools (plasmid_annotate vs plasmid_deep_annotate) which could cause confusion.

Naming Consistency3/5

The naming pattern is largely consistent with snake_case verb_noun or noun_descriptor (e.g., primer_design, plasmid_annotate, fastq_trim). However, there are exceptions like 'batch', 'workflow', 'gc_content', and 'cloning_diagnose' which don't follow the verb_noun pattern consistently. Also, some names are phrases like 'golden_gate_from_parts'.

Tool Count2/5

With 101 tools, this server is extremely large and likely overwhelming for agents. Even for a comprehensive bioinformatics toolkit, this exceeds a manageable scope, risking agent confusion and inefficient tool selection. A more modular approach would be advisable.

Completeness4/5

The tool surface covers a wide range of bioinformatics workflows including sequence analysis, primer design, cloning, CRISPR, NGS, expression analysis, and data export. There are minor gaps such as lack of a dedicated protein structure prediction tool and limited off-target genome coverage, but overall the set is impressively complete for its domain.

Resources