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Oligo cofold (ΔG)

oligo_cofold
Read-onlyIdempotent

Minimum-free-energy structure and ΔG for one oligo (hairpin) or two oligos together (homo/heterodimer), using ViennaRNA's published loop model at a temperature you choose — DNA parameters (Mathews 2004) by default, RNA (Turner 2004) on request. Reports each strand alone, the duplex, and the interaction ΔG the two gain by pairing with each other rather than folding alone, which is the number a primer-dimer screen wants. Unlike oligo_analysis's fast stack-sum screen this is a full loop model with bulge, internal-loop and dangling-end terms; the two are on different parameter sets and must not be compared. PREDICTED, NOT MEASURED. No skill statistic is claimed for predicting whether a PCR fails. Loop-model MFE folding reproduces measured structure well for short duplexes and progressively worse with length; the ΔG itself carries roughly kcal/mol-scale uncertainty and the MFE structure is one structure out of an ensemble — request partition for the ensemble free energy, which is the more honest single number when several structures compete. Valid for: short oligos, at most 200 nt per strand, at the temperature given. It models two strands in isolation at no particular concentration: it does not know your primer concentration, salt, or cycling programme, so it cannot say whether a dimer will actually form in your tube.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aYesFirst oligo, 5'→3'. Max 200 nt.
bNoSecond oligo. Omit to analyse hairpin structure in 'a' alone; pass the same sequence as 'a' for a homodimer.
alphabetNoWhich measured parameter set to use. This is not cosmetic — the same 20-mer can differ by several kcal/mol between them.dna
partitionNoAlso compute the ensemble free energy over all structures, not just the MFE one. Costs a second pass.
temperatureNo°C. Primer dimers matter at the annealing temperature, not at 37 °C.

TDQS

A4.8/5.0
Behavior5/5

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

Goes well beyond annotations (readOnlyHint, idempotentHint) by disclosing that results are predicted, not measured; that ΔG has ~kcal/mol uncertainty; that MFE structure accuracy degrades with length; and that the model ignores concentration and salt. 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.

Conciseness4/5

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

Description is fairly long but every sentence adds essential information: purpose, output, comparison, limitations, validity range, parameter usage advice. Front-loaded with core purpose. Could be slightly more streamlined, but no fluff.

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

Completeness5/5

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

Given the complexity of the tool and absence of output schema, the description thoroughly covers what it computes, parameter sets, temperature, comparators, limitations, validity (≤200 nt), and what it cannot do (ignore concentration, salt, cycling). Provides sufficient context for an agent to use and interpret results.

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 already covers all 5 parameters with high-quality descriptions (100% coverage). Description adds contextual value for temperature (should be annealing temperature) and partition (costs second pass, more honest for competing structures). Modest added value beyond schema, justifying a score above baseline 3.

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?

Description explicitly states it computes minimum-free-energy structure and ΔG for one or two oligos (hairpin/dimer), contrasting with sibling oligo_analysis's fast stack-sum screen. It clearly defines the resource and action, and avoids tautology.

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

Usage Guidelines5/5

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

Provides explicit when-to-use guidance: full loop model for short oligos (≤200 nt), contrasts with oligo_analysis, warns about uncertainty and limitations (not measured, no PCR failure prediction, ignores concentration/salt/cycling), and advises using partition for ensemble free energy when competing structures exist.

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