workbench

Golden Gate overhang fidelity

golden_gate_fidelity

Read-onlyIdempotent

Score a candidate set of 4-base Golden Gate/MoClo junction overhangs against real published T4-ligase ligation-count data: per-overhang specificity, the weakest link in the set, and any risky cross-reacting pairs. Optionally compare against a named published overhang set. This is SeqBench's own transparent scoring methodology — it does not reproduce NEB's/Potapov's own published aggregate fidelity percentages for named sets (their exact formula isn't disclosed anywhere accessible).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`dataset`	No	Which real ligation dataset to score against — generic T4 ligase, or an enzyme-specific one-pot dataset if that matches your actual digestion enzyme.	generic-t4-37c-1h
`overhangs`	Yes	The candidate 4-base overhangs for one assembly (e.g. ["GGAG","TACT","AATG"]). At least 2, no duplicates.
`riskThreshold`	No	Flag a pair as risky when the cross-reaction is at least this fraction of that pair's own total signal.
`compareToNamedSet`	No	Also score this published reference set (see namedSetsAvailable in the output) alongside your candidate set, for comparison.

TDQS

A4.2/5.0

Behavior4/5

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

Annotations indicate read-only and idempotent behavior, and the description adds context about the transparent methodology and its limitations (not reproducing NEB's exact values). No contradictions. The description enhances understanding 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 two sentences long with no filler. The first sentence clearly states the main function and outputs, while the second clarifies limitations. Very efficient.

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?

Given the complexity (multiple parameters, optional comparisons, no output schema), the description covers the essential workflow, inputs, and what the tool does not provide. It is sufficient for an agent to understand usage without needing the output schema.

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 coverage is 100% with each parameter described well. The description does not add new meaning beyond the schema, but provides context for the overall scoring purpose. Baseline 3 is appropriate.

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 clearly states the tool scores candidate overhang sets against real ligation data, enumerates specific outputs (specificity, weakest link, risky pairs), and distinguishes itself from alternatives like NEB's undisclosed methodology. It is specific and sets clear boundaries.

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 description mentions optional comparison with named sets but does not explicitly cover when not to use the tool or offer direct alternatives. However, it does clarify what the tool does not do (reproduce NEB percentages), which helps guide appropriate use.

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.

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