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Jean — Molecular Biology & Genomics

back_translate

Read-only

Protein → DNA: the coding sequence that would encode a protein, weighted by a real codon-usage table.

Input Schema

NameRequiredDescriptionDefault
hostNocodon-usage table to weight by, named as the workbench knows it. Required for the two table-driven strategies and ignored by the other two. Tables loaded in the Back-translate or Optimizer panel this session are valid names as well as the bundled ones. Only applies when strategy is mostFrequent or weightedRandom.E. coli K-12
seedNoseed for the usage-weighted draw, which makes a sampled design reproducible. Omit for an unseeded draw; ignored by the three deterministic rules. Only applies when strategy is weightedRandom.
tableNogenetic code the codons are chosen under.Standard
_filesNoThe project: record references shaped like _subject. Their records become library documents a tool names in sequences/parts/document; a trace or alignment is found by file name.
proteinYesthe protein sequence, one-letter codes (B/Z/J/U/O/X and gaps are handled and reported); wire it from translate_sequence.
_subjectNoThe record to act on: exactly one of sequence, accession (+source), sample (an id from list_samples) or file ({name, text} or {name, base64}); record_index picks one record of several.
strategyNomostFrequent — EMBOSS backtranseq, and SnapGene's “Preferred Codons”: every residue takes its host's single most-used codon. | weightedRandom — Each residue is drawn from its family's real frequency distribution: a natural-looking gene rather than a run of one codon. | ambiguous — EMBOSS backtranambig: one ambiguity-coded codon covering EVERY codon for the residue. The degenerate-primer answer; needs no usage table. | firstCodon — DnaChisel's reverse_translate. Deterministic and table-free, but it is not a biological preference: use it only to reproduce a DnaChisel result.mostFrequent
append_stopNoappend a stop codon, chosen by the same strategy and table as every other codon. Skipped when the residues already end in *.

Output Schema

NameRequiredDescriptionDefault
gcYesGC fraction of the product (0–1), over unambiguous bases only
dnaYesthe coding sequence — wire it into optimize_sequence, or open it as a record
cautionsYesonly the caveats THIS product earned, each with its measurement and whose published limit it crossed — an empty list is the healthy case and is not padded
residuesYesevery residue with the codon chosen, its share of its synonymous family, and why if it took a special path
conclusionYesone generated sentence saying what this product IS — size, GC, and how adapted it is to the table you named. Built from the run by the same `conclusionOf` the panel headlines, so a turn and the panel cannot state different things about one design
provenanceYesthe assumptions the answer rests on: strategy, genetic code, codon-usage table and residue count — the same facts the panel prints under the product
undefinedResiduesYesresidues the genetic code has no codon for; each became NNN
unweightedResiduesYesresidues whose synonymous family carried NO weight in this table, so codon order decided the answer rather than usage — empty is the healthy case

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so safety is covered and the bar is lower. The description adds that output codons are weighted by a real codon-usage table, implying non-determinism for at least one mode, but it does not mention reproducibility via seed or which modes are deterministic — that lives only in the schema.

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?

A single front-loaded sentence with the input and output on either side of an arrow and one qualifier. Nothing is wasted and the core semantics arrive first.

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?

An output schema exists and the parameter schema is exhaustive, so return values and per-parameter semantics need not be restated. Still, for a tool with eight parameters, four strategies, and project/record plumbing, the one-sentence description leaves the agent dependent entirely on schema text to call it correctly.

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% and the enum/strategy documentation is unusually rich, so the schema carries the parameter burden. The description adds no meaning beyond it — no detail on host table selection, seed behavior, or the _subject/_files project wiring. Baseline 3 is appropriate.

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

Purpose4/5

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

The description states a specific transformation with directionality ('Protein → DNA: the coding sequence that would encode a protein'), which implicitly contrasts with the reverse-direction sibling translate_sequence. It never names that sibling or optimize_sequence explicitly, so differentiation is inferable rather than stated.

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

Usage Guidelines2/5

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

There is no when-to-use, when-not-to-use, or alternative-tool guidance in the description itself; the agent must open the schema to discover that four strategies exist and which one to pick. The phrase 'weighted by a real codon-usage table' hints that sampling is involved but does not route the agent among back_translate, optimize_sequence, or codon_optimality.

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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