jean — Molecular Biology & Genomics

optimize_sequence

Run the DnaChisel optimizer over the subject with real constraints/objectives (GC window, pattern avoidance, translation enforcement, …). Emits the optimized sequence as a NEW record for downstream nodes.

Input Schema

TableJSON Schema

Name	Required	Description
`seed`	No	base seed for the alternatives — the same seed returns the same set. Advanced.
`_files`	No	The 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.
`_subject`	No	The 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.
`candidates`	No	how many alternative solutions to produce (1 = a single solve). Candidate 1 is the plain unseeded answer; the rest run at seed+1, seed+2, …, so a listed alternative can be asked for again. Max 12.
`objectives`	No	DnaChisel objective specs — soft goals the solver maximises. Same `kind` values as constraints: EnforceTranslation, AvoidPattern, EnforceGCContent, AvoidHairpins, AvoidRareCodons, UniquifyAllKmers, CodonOptimize, EnforceSequence, AvoidChanges, EnforceChoice, EnforcePatternOccurence, AvoidStopCodons, EnforceMeltingTemperature, EnforceChanges, EnforceTerminalGCContent, SequenceLengthBounds, EnforceRbsStrength, AvoidHeterodimerization, AllowPrimer, EnforceRegionsCompatibility, AvoidMatches, AvoidBlastMatches.
`constraints`	No	DnaChisel constraint specs, e.g. [{"kind":"EnforceGCContent","mini":0.4,"maxi":0.6,"window":50}] — hard requirements the solution must satisfy. Valid `kind` values: EnforceTranslation, AvoidPattern, EnforceGCContent, AvoidHairpins, AvoidRareCodons, UniquifyAllKmers, CodonOptimize, EnforceSequence, AvoidChanges, EnforceChoice, EnforcePatternOccurence, AvoidStopCodons, EnforceMeltingTemperature, EnforceChanges, EnforceTerminalGCContent, SequenceLengthBounds, EnforceRbsStrength, AvoidHeterodimerization, AllowPrimer, EnforceRegionsCompatibility, AvoidMatches, AvoidBlastMatches.
`randomization_threshold`	No	DnaChisel `randomization_threshold` (default 10000). LOWER it to force the stochastic search: an objective-driven problem is deterministic, so without this every candidate comes back identical. Advanced.

Output Schema

TableJSON Schema

Name	Required	Description
`nEdits`	Yes	nucleotides changed
`record`	Yes	the optimized sequence as a record
`gcAfter`	Yes	GC fraction after optimization
`success`	Yes	all hard constraints satisfied
`caiAfter`	Yes	CAI after optimization, or null when not computable (no codon table to score against): a 0 would read as the worst possible adaptation
`distinct`	Yes	how many of the candidates are DIFFERENT sequences — 1 means the problem has a single answer, not that the run failed. The engine omits it for a single-candidate run, where it is 1 by definition
`sequence`	Yes	the optimized sequence
`candidates`	Yes	every alternative solution: its seed, sequence, edit count, GC and CAI — compare them before choosing. Empty unless `candidates` asks for more than one

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 20, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already indicate this is not read-only and not destructive. The description adds meaningful context beyond that: it emits the optimized sequence as a NEW record for downstream nodes, which is a concrete behavioral outcome. It does not discuss computational cost, determinism controls, or permissions, but with annotations present this is a solid addition.

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?

Two compact sentences, front-loaded with the core action and followed by the output behavior. Every phrase carries information and there is no filler.

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 optimization tool with a full input schema, annotations, and an output schema, the description covers the essential purpose and output shape. It is complete enough for invocation, though it lacks guidance on selection relative to sibling optimization tools.

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%, so all 7 parameters are already documented in detail. The description only mentions constraints and objectives with a few examples that overlap with the schema's own enumerations; it adds no extra meaning for seed, candidates, randomization_threshold, or _files. 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 verb and resource: 'Run the DnaChisel optimizer over the subject with real constraints/objectives.' It also names the output ('optimized sequence as a NEW record'), so the agent can distinguish it from generic sequence tools. However, it does not explicitly differentiate itself from sibling tools like codon_optimality or design_expression_variants.

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 phrase 'with real constraints/objectives' implies the tool is for constrained sequence optimization, but there is no explicit when-to-use guidance, prerequisites, or named alternatives. The context is inferable from the examples (GC window, pattern avoidance, translation enforcement) but the agent is not told when to choose this over other design or codon tools.

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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