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Cloning failure diagnosis (ranked causes from design + observation)

cloning_diagnose
Read-onlyIdempotent

Work out why a cloning experiment failed: no colonies, every clone empty vector, or no PCR band. Takes your design (method, parts, enzymes, primers, host methylation state) plus what you actually observed (colony counts on the plate and on each control, screening tally, band sizes, whether the ladder ran) and returns causes ranked by evidence — each with the deterministic fact from the design or the observation that implicates it, the cheapest observation that would separate it from the next candidate, and the next experiment. Causes the observations eliminate are reported as eliminated, naming the observation that did it; causes the design makes impossible are not listed. No probability is computed anywhere — the ordering is of evidence, not of likelihood, and ranking.evidenceBased says so when the inputs separate nothing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
partsNoParts in assembly order (Golden Gate / Gibson), or [vector, insert] for a restriction ligation. Raw or FASTA. Up to 8; 300,000 bp total across parts and template.
methodYesHow the construct was made. Decides which cause set applies and which design checks can run.
enzymesNoThe Type IIS enzyme for Golden Gate (BsaI, BbsI, Esp3I (BsmBI)), or the one or two restriction enzymes the insert and vector were cut with. An unrecognised name is rejected rather than skipped: every methylation and site-geometry verdict here reads "no site found" when the enzyme is missing, so a typo would come back as a clean bill of health.
symptomYesThe complaint. "no_colonies" = the plate is blank; "wrong_clones" = colonies grew but the clones are empty vector / rearranged / the original template; "no_pcr_band" = the gel lane is empty or the band is the wrong size.
coloniesNoColonies on the experimental plate. Omitting this is different from giving 0.
screenedNoScreening tally, up to 100,000 clones. The buckets must not add up to more than total — an inconsistent tally is rejected rather than reasoned over, because every verdict drawn from the screen compares a count against that total.
templateNoPCR template, raw or FASTA.
ladderRanNoWas the ladder itself visible in the same gel? A true here eliminates the whole stain-and-imaging branch in one observation.
partNamesNoLabels for the parts, in the same order ("backbone", "promoter", "CDS"). Used in every fact that names a part.
overlapLenNoGibson only — the homology arm length the assembly was designed with.
insertSourceNoWhere the insert came from. Only "plasmid_pcr" changes a verdict: a plasmid template replicates and selects on its own, so it can give colonies with no ligation at all.
vectorMarkerNoSelection marker on the vector. Compared against the plate's antibiotic by identity, not by guessing at free text.
primerForwardNoForward primer, up to 200 nt.
primerReverseNoReverse primer, up to 200 nt.
agarosePercentNoAgarose percentage of the gel. Snapped to the nearest tabulated value; decides the resolving window a band is placed against.
annealingTempCNoAnnealing temperature actually used, °C. Compared against each primer's own nearest-neighbour Tm.
hostMethylationNoDam/Dcm state of the DNA that was CUT — a property of the strain the plasmid was prepared from, not the one it was transformed into. Standard cloning strains are dam+/dcm+. "dam_dcm_minus" eliminates every methylation-blocking cause; "unknown" leaves the sequence overlap stated as a fact and the methylation as an open question.unknown
plateAntibioticNoAntibiotic on the plate. amp and carbenicillin count as the same selection (both select bla).
templateCircularNoTreat the template as a plasmid, so a product across the origin is found.
gelBandsObservedBpNoBand sizes read off the lane, bp. An empty array ("I looked and there was nothing") is not the same as omitting the field.
phosphataseTreatedNoWas the cut vector dephosphorylated (rSAP/CIP) before ligation?
noDnaControlColoniesNoColonies from the same cells and plate with NO DNA added. Non-zero means the plate is not selecting.
noInsertControlColoniesNoColonies from the identical reaction with the insert (or one part) left out.
positiveControlColoniesNoColonies from an intact, uncut plasmid into the same cell aliquot. Non-zero eliminates "the cells never took up DNA".
positiveControlSameMarkerNoDid that control plasmid carry the SAME selection marker? If not, it eliminates the cells but says nothing about the plate — the tool reports the difference.
uncutVectorControlColoniesNoColonies from the digested but UNLIGATED vector. Measures intact vector that survived the digest, with no help from the ligase.
transformationEfficiencyCfuPerUgNocfu/µg from a control transformation, where it was measured.

TDQS

A4.6/5.0
Behavior5/5

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

The description adds substantial behavior beyond the annotations: it discloses that ordering is by evidence, not likelihood; that no probability is computed; that eliminated causes are reported with the eliminating observation; that design-impossible causes are omitted; and that inputs are rejected rather than skipped. All of this is consistent with readOnlyHint=true, openWorldHint=false, and idempotentHint=true — no contradiction.

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?

The description is long, but the tool has 27 parameters and no output schema, so the density is largely justified; every sentence carries behavioral or output-format content. It is front-loaded with purpose, and the only mild redundancy is the input enumeration, which earns its place by establishing the design/observation split.

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?

With no output schema, the description carries the full burden of explaining the result shape, and it does so thoroughly: ranked causes with the implicating fact, the cheapest separating observation, the next experiment, eliminated causes, and excluded causes. Nothing an agent needs to know to invoke the tool or interpret its response is missing.

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 coverage is 100%, so the baseline is 3, and the description correctly does not repeat per-parameter definitions. It adds a schema-independent organizing layer by partitioning inputs into design (method, parts, enzymes, primers, host methylation) versus observation (colony counts, screening tally, band sizes, ladder ran), which helps an agent understand which parameters drive verdicts.

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 opens with a specific verb-resource pair ('Work out why a cloning experiment failed') and enumerates the three accepted symptoms, leaving no ambiguity about scope. It clearly distinguishes this from sibling cloning_simulate: the agent is told this tool consumes both design and observed outcomes and returns ranked causes, whereas simulation covers forward prediction.

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 makes the trigger condition explicit — a failed experiment matching one of the three symptom categories — and implicitly excludes related siblings (virtual_gel, in_silico_pcr, cloning_simulate) by describing its input/output contract. It stops short of explicitly naming alternatives or stating 'use this instead of X,' which would make routing fully unambiguous.

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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