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jean — Molecular Biology & Genomics

build_assembly_report

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Build DnaCauldron's assembly report from an assembly result: the per-construct summary table, the constructs and errors spreadsheets as pandas-compatible CSV, and the Outcome / Number of assemblies stats. Wire golden_gate_assemble or gibson_assemble's products, errors and warnings outputs into it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
_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.
enzymeNothe Type-IIS enzyme, for a method that has one — it becomes the report's `enzymes` column, mirroring DnaCauldron's `Type2sRestrictionAssembly.get_extra_construct_data()`. Leave it empty for Gibson, which has no enzyme: an empty column would imply one exists.
errorsNostructured assembly errors — wire the assembly node's `errors` output. These become the errors spreadsheet, which is the most useful thing in a report about a run that did not work.
_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.
productsNothe enumerated constructs — wire an assembly node's `products` output. Each needs `id`, `sequence` and `fragmentNames`, which is what both assembly nodes emit.
warningsNostructured assembly warnings — wire the assembly node's `warnings` output.
assembly_nameNothe name for this assembly: the `assembly_name` column of the summary table and the first field of every errors row.assembly

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statsYesthe Outcome / Number of assemblies table, as `compute_stats` produces it
reportYesthe whole report object, including the summary dataframe and the plots decision
errorsCsvYesthe errors spreadsheet — upstream's `errors.csv`, or an empty string when the run had none. Faithful to upstream's own 4-header / 3-field mismatch
partsUsedYesevery part name that appears in at least one construct
summaryCsvYesthe constructs spreadsheet, pandas-compatible CRLF CSV — upstream's `constructs.csv`
summaryTextYesthe report as plain text, for a run record or a note
warningsCsvYesthe warnings spreadsheet, or an empty string when the run had none
constructCountYeshow many constructs the summary table describes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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. The description adds genuine behavioral context beyond that: output is 'pandas-compatible CSV', the report is composed of named artifacts, and the errors spreadsheet is characterized as the most useful output for a failed run. It does not spell out return structure, but an output schema exists.

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?

Two sentences, front-loaded with the core action and then the wiring instruction; every clause carries information (report contents, output format, upstream inputs). It is dense but not padded. Minor density from the enumerated artifacts keeps it just short of ideal.

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 a rich 7-parameter schema at 100% coverage, existing annotations, and an output schema, the description supplies the missing cross-tool wiring and format notes an agent needs to call it correctly. Nothing critical is absent, though a brief note on ordering (assemble first, then build report) would make it fully self-contained.

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 description coverage is 100%, so baseline is 3; the description adds value by tying parameters to report semantics (the enzyme becomes the report's `enzymes` column and should be left empty for Gibson to avoid implying an enzyme exists) and by specifying the required shape of `products` (id, sequence, fragmentNames). This is meaning beyond the schema, though not exhaustive for every parameter.

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?

States a specific verb and resource ('Build DnaCauldron's assembly report from an assembly result') and enumerates exactly what the report contains: the per-construct summary table, the constructs and errors spreadsheets as pandas-compatible CSV, and the Outcome / Number of assemblies stats. It also names the upstream assembly nodes it consumes, so an agent can distinguish it from unrelated report/export siblings.

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 second sentence gives explicit wiring context: 'Wire golden_gate_assemble or gibson_assemble's products, errors and warnings outputs into it', which tells the agent when this tool is appropriate (after an assembly run) and which sibling outputs feed it. It stops short of stating exclusions (e.g. when a report is unnecessary), so it is clear context rather than full when/when-not 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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