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jean — Molecular Biology & Genomics

tide_quantify_indels

Read-only

Quantify a CRISPR edit from a pair of Sanger traces by TIDE (Brinkman et al.) — the abundance of each indel SIZE in the sequenced population, with a p-value per size class, the total editing efficiency, the goodness of fit, and the base composition of the +1 insertion. This is a different question from crispr_quantify_edits, not a cheaper version of it: ICE fits a mixture of whole proposed alleles and names them, TIDE fits the control trace shifted by n bases and so reports how much of the population shifted by n, whatever allele carried it. Run both on the same pair when the two disagree about how much was edited — that disagreement is information about the read.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
guideYesthe guide spacer, RNA accepted. TIDE decomposes around ONE break, so this is a single guide rather than a list — it is located in the control on either strand and the break is placed three bases 5-prime of the PAM.
_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.
editedYesthe EDITED trace, by file name or id.
controlYesthe UNEDITED trace, by file name or id — every indel class is the control trace shifted, so this read IS the model.
seq_endNolast base of the READ to use, clamped down to the shorter of the two reads if it overruns. In bp.
_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.
max_shiftNolargest indel size modeled, in both directions. One basis function is fitted per size from -max_shift to +max_shift, so this bounds what can be reported at all. In bp.
seq_startNofirst base of the READ to use. The start of a Sanger read is poor quality; upstream's own web app starts at 100. In bp.
window_endNolast base of the decomposed region. 0 = as far as the shorter read allows. In bp. Advanced.
p_thresholdNoan indel class is called PRESENT below this p-value. A 0% class and a non-significant class are different answers, which is why this is a knob rather than a constant. Advanced.
window_startNofirst base of the decomposed region. 0 asks the engine to place the window after the break itself, which is what upstream does when given no boundaries. In bp. Advanced.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
indelsYesevery modeled size from -max_shift to +max_shift with its percentage, p-value and whether it cleared the threshold — INCLUDING the classes that did not, so the spectrum has a shape and not only peaks
reportYesthe whole result, including the window corrections upstream raises as warnings
rSquaredYesgoodness of fit of the whole decomposition. Every percentage below is already scaled by it, so a low fit shrinks the classes rather than flagging them
breaksiteYesthe base the break was placed at, three bases 5-prime of the PAM. Every window in the result is positioned against it
efficiencyYestotal editing efficiency: the R-squared-scaled signal in every non-zero indel class, 0-100. Not the same quantity as an ICE score — wire them into a comparison rather than treating either as the number
aberrantSignalYesthe four means TIDE's own quality checklist is read from: control and sample, before and after the break. A sample already aberrant BEFORE the break is a sequencing problem rather than an edit, and this is the pair of numbers that says so
insertionBasesYesbase composition of the +1 insertion as percentages in A, C, G, T order — the readout ICE cannot produce. Null when there is no significant +1 class, which is the common case and not an error; `insertionNote` then says why in upstream’s own words
significantIndelsYesthe classes that cleared p_threshold, excluding the unmodified class — what a gate should read

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds genuine behavioral context: TIDE fits the control trace shifted by n bases and decomposes around ONE break, and that the control read IS the model. It does not add auth/rate-limit or failure-mode detail, but for a read-only analysis tool this is solid added context.

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 dense sentences, front-loaded with the purpose before the ICE/TIDE contrast. Every clause carries information, though the em-dash chains make it slightly heavy to parse.

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 not be re-explained, and the description still conveys the analytical model and the relation to the sibling tool. An agent has everything needed to call it correctly and interpret when it matters.

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 schema itself is highly descriptive (guide, seq_start, window, max_shift, p_threshold all explained). The description only supplementally reinforces the model concept behind the control parameter, so the schema does the heavy lifting and baseline 3 applies.

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 ("Quantify a CRISPR edit from a pair of Sanger traces by TIDE"), names the method, and enumerates the reported outputs (indel-size abundance, per-size p-value, editing efficiency, goodness of fit, +1 insertion composition). It explicitly distinguishes itself from the sibling crispr_quantify_edits.

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

Usage Guidelines5/5

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

Names the alternative (ICE / crispr_quantify_edits) and the exact condition for considering both: "Run both on the same pair when the two disagree about how much was edited." It also explains why they answer different questions rather than just ranking one above the other.

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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