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jean — Molecular Biology & Genomics

bootstrap_cq

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Resampling error bars on ONE reaction's Cq. A Cq is read off a fitted model, so its uncertainty is how tightly the observations pin that model down - which propagating a measured spread cannot answer. This refits the curve a few hundred times, either by resampling its residuals or by leaving observations out, and reports the spread of everything the fit produces: every model coefficient, the Cq under each definition, the efficiency and the initial fluorescence, each with the quantiles of the iterations rather than a normal approximation, so an asymmetric interval means the estimate genuinely is asymmetric. Reproduces qpcR 1.4.2's pcrboot.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dropNojackknife only: observations left out of each refit. 1 is the delete-one jackknife; more is a delete-d jackknife, which is less sensitive to a single influential cycle. Only applies when scheme is jack.
seedNoR's `set.seed` value, so the interval is reproducible and citable. Advanced.
modelNothe sigmoid fitted once and REFITTED on every iteration. It is not re-chosen per resample: Cqs fitted under different models are not comparable, so a mixture over models would make the quantiles mean nothing. Values: l4 (l4 · 4-param log-logistic), l5 (l5 · + asymmetry), l6 (l6 · + linear baseline drift), l7 (l7 · + quadratic baseline), b4 (b4 · 4-param logistic (raw scale)), b5 (b5 · + asymmetry), b6 (b6 · + linear baseline drift), b7 (b7 · + quadratic baseline).l4
_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.
curvesYesthe amplification table: a header row, first column the cycle number and every other column one reaction's fluorescence. The same parser `call_cq` and the Curve->Cq tab use.
schemeNo`boot` resamples the fit's residuals (upstream's default); `jack` leaves observations out. The bootstrap answers "how would this Cq move under the same noise?", the jackknife "how much does any single cycle carry it?".boot
_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.
reactionNowhich reaction to resample, by its column name. Blank takes the first column — this is a per-reaction run by design, because at the default 200 iterations a 47-reaction plate would be 9,400 refits nobody asked for.
cq_methodNowhich Cq definition the resampled efficiency readouts report. Values: cpD2 (cpD2 · second-derivative max), cpD1 (cpD1 · first-derivative max), maxE (maxE · cycle of maximum efficiency), expR (expR · exponential region), Cy0 (Cy0 · tangent x-intercept (Guescini)), CQ (CQ · 20 % of the Cq fluorescence), maxRatio (maxRatio · spline, model-free (Shain)).cpD2
conf_levelNothe level the reported bounds are read at — 0.95 puts them at the 2.5 % and 97.5 % quantiles of the iterations.
iterationsNoeach iteration is a full nonlinear refit, so this is the run's whole cost. 100 is upstream's default and puts a 95 % bound on a single order statistic, which is why both doors open on 200.
include_readoutsNoalso resample every efficiency readout — the Cq definitions, the efficiency, the initial fluorescence — not just the model coefficients. On, because an interval on the Cq is the point; off is faster and returns coefficients alone. Advanced.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rssYesresidual sum of squares across the iterations
rmseYesresidual standard error across the iterations
modelYesthe model refitted on every iteration
reactionYesthe reaction actually resampled, named — "the first column" is not a citation
readoutsYesone row per resampled readout — Cq under each definition, efficiency, initial fluorescence — with mean, SD, median and the interval quantiles. Empty when `include_readouts` is off
confLevelYesthe level the bounds above are read at
convergedYesiterations whose refit converged — the ONLY ones any number here is computed from. Well below `iterations` is a finding about the curve, not a warning to swallow
iterationsYesiterations requested
coefficientsYesone row per model coefficient, in the model's own parameter order, same fields

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already carry the safe-read profile (readOnlyHint/destructiveHint false, openWorldHint false), and the description adds substantial behavior beyond that: it refits a few hundred times, iterations is the run's whole cost, and per-reaction design avoids 9,400 refits. It also explains that a fixed model is refit per iteration so Cqs stay comparable.

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?

Front-loaded with the core purpose and the differentiator against error propagation, and every sentence is substantive. It is dense and somewhat long, with a couple of explanatory clauses that could be trimmed, but nothing is padding.

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 an output schema present, return values need no prose. The description covers method, scheme choice, model-refit semantics, cost, and the upstream provenance (qpcR 1.4.2 pcrboot), leaving nothing an agent needs to call it correctly unstated.

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 baseline is 3, but the description adds conceptual meaning: why iterations maps to cost and quantile resolution, what boot vs jack actually resample, and the rationale for include_readouts being on. Most of this is echoed in the schema descriptions, so it adds value but not a full dimension above.

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 ('Resampling error bars on ONE reaction's Cq') and immediately scopes the statistical problem it solves ('how tightly the observations pin that model down'). An agent can distinguish this from call_cq (which just reads off a Cq) and propagate_uncertainty without opening any schema.

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?

Gives clear context (per-reaction by design, boot vs jack semantics, when each scheme answers a different question) and implicitly rules out error-bar approaches that propagate a measured spread. It lacks an explicit named alternative or a 'do not use this for X' exclusion, so it stops short of a 5.

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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