workbench

Construct auto-fix (domestication)

construct_autofix

Read-onlyIdempotent

Iteratively substitutes synonymous codons to resolve unwanted restriction sites (domestication for Golden Gate), homopolymers, tandem repeats, predicted secondary structure, cryptic RBS/polyA motifs and hidden alternate-frame ORFs that construct_qc flags — without changing the encoded protein (verified). Does NOT touch premature stops or GC extremes; re-run construct_qc afterward to confirm. A native TypeScript alternative to a constraint-solver sidecar.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`gcLow`	No
`gcHigh`	No
`gcWindow`	No
`organism`	No	Codon-usage table to prefer among synonymous options.	ecoli
`sequence`	Yes	Nucleotide sequence (raw or FASTA; IUPAC accepted).
`maxPasses`	No	Repeat full passes until clean or no further progress.
`frameStart`	No	1-based nucleotide where the reading frame begins.
`avoidEnzymes`	No	Enzyme names whose internal sites should be removed (e.g. ["BsaI","BsmBI"] for Golden Gate domestication).
`homopolymerMin`	No
`crypticOrfMinAa`	No	Minimum peptide length (aa) for a hidden alternate-frame ORF to be flagged.

TDQS

B3.4/5.0

Behavior1/5

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

The description contradicts the annotation readOnlyHint=true because the tool clearly modifies the sequence (substitutes codons). According to the rule, a score of 1 is given when description contradicts annotations. The description itself is transparent, but the contradiction undermines trust.

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 extremely concise with four sentences covering purpose, scope, limitations, and context. Every sentence earns its place with no redundancy.

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

Completeness3/5

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

Given the complexity (10 parameters, no output schema) and the annotations with contradictions, the description covers the main purpose and limitations but lacks details on output format, error cases, and parameter interplay. It is minimally adequate but leaves gaps.

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

Parameters1/5

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

Schema description coverage is 60%, but the tool description adds no additional information about the 10 parameters. The description does not explain parameter roles, defaults, or interactions beyond what is in the schema, so it fails to add value.

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 iteratively substitutes synonymous codons to resolve unwanted restriction sites and other issues flagged by construct_qc, without changing the encoded protein. It distinguishes itself from sibling tools like construct_qc and codon_optimize by focusing on fix-ups after QC.

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?

The description explicitly advises to re-run construct_qc afterward, implies it should be used after construct_qc detects problems, and states what it does NOT touch (premature stops, GC extremes). This provides clear when-to-use and when-not-to-use guidance.

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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