Jean — Molecular Biology & Genomics

rank_tradeoffs

Read-only

Which candidates in a table are not beaten on every objective at once: Pareto (non-dominated) sorting of rows scored on several columns, each to maximize or minimize.

Input Schema

Name	Required	Description	Default
`label`	No	the column naming each candidate. Left unset, the first column.
`order`	No	what orders candidates within a front: crowding distance (how isolated each is), or exclusive hypervolume contribution (how much of the objective space the front loses without it). Values: crowding (crowding distance), hypervolume (hypervolume contribution).	crowding
`table`	Yes	the candidates: a header row, then one row per candidate. Comma- or tab-separated, quoted cells allowed.
`_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.
`filter`	No	keep only rows passing every clause, separated by commas: a column, one of = != >= <= > <, and a value (`em_max>=580, agg=m`). Numbers compare as numbers; a blank cell passes no ordering clause.
`_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.
`crowding`	No	how isolation along a front is measured: NSGA-II's normalized distance (each gap over the objective's range, averaged), or the raw sum of gaps in each objective's own units. Values: normalised (NSGA-II (normalized)), raw (raw gaps). Advanced.	normalised
`maximise`	No	the columns where higher is better, separated by commas.
`minimise`	No	the columns where lower is better, separated by commas.
`reference`	No	the hypervolume's reference point, one `column=value` per objective (`brightness=0, maturation=500`), each worse than every candidate. Left unset, each objective's worst value pushed out by 10% of its range. Only applies when order is hypervolume. Advanced.
`keep_duplicates`	No	keep every copy of candidates with identical values on the first front; off keeps the first copy and names it as what the others duplicate. Advanced.

Output Schema

Name	Required	Description
`front`	Yes	the non-dominated candidates (front 1), most isolated first
`order`	Yes	what ordered candidates within a front: crowding or hypervolume
`counts`	Yes	rows in the table, filtered out, left out for an empty objective, ranked
`fronts`	Yes	how many candidates sit on each front
`ranked`	Yes	every candidate ranked: front, flag, crowding distance, the candidate that beats it, its objective values
`reference`	Yes	the reference point the hypervolume was measured from, and whether it was derived (hypervolume order only)
`hypervolume`	Yes	the objective space front 1 dominates up to the reference point (hypervolume order only)

Schema Changelog

Changes observed during successful MCP inspections.

Changed7 schema fields changedOct 21, 2026

changedInput schema / properties / crowding / description

Previous value: -"how isolation along a front is measured: NSGA-II's normalised distance (each gap over the objective's range, averaged), or the raw sum of gaps in each objective's own units. Values: normalised (NSGA-II (normalised)), raw (raw gaps). Advanced."New value: +"how isolation along a front is measured: NSGA-II's normalized distance (each gap over the objective's range, averaged), or the raw sum of gaps in each objective's own units. Values: normalised (NSGA-II (normalized)), raw (raw gaps). Advanced."

addedInput schema / properties / order

Added value: +{
+  "default": "crowding",
+  "description": "what orders candidates within a front: crowding distance (how isolated each is), or exclusive hypervolume contribution (how much of the objective space the front loses without it). Values: crowding (crowding distance), hypervolume (hypervolume contribution).",
+  "enum": [
+    "crowding",
+    "hypervolume"
+  ],
+  "type": "string"
+}

addedInput schema / properties / reference

Added value: +{
+  "description": "the hypervolume's reference point, one `column=value` per objective (`brightness=0, maturation=500`), each worse than every candidate. Left unset, each objective's worst value pushed out by 10% of its range. Only applies when order is hypervolume. Advanced.",
+  "type": "string"
+}

addedOutput schema / properties / hypervolume

Added value: +{
+  "description": "the objective space front 1 dominates up to the reference point (hypervolume order only)",
+  "type": [
+    "number",
+    "null"
+  ]
+}

addedOutput schema / properties / order

Added value: +{
+  "description": "what ordered candidates within a front: crowding or hypervolume",
+  "type": [
+    "string",
+    "null"
+  ]
+}

addedOutput schema / properties / reference

Added value: +{
+  "description": "the reference point the hypervolume was measured from, and whether it was derived (hypervolume order only)"
+}

changedOutput schema / required

Previous value: -[
-  "front",
-  "ranked",
-  "fronts",
-  "counts"
-]New value: +[
+  "front",
+  "ranked",
+  "fronts",
+  "counts",
+  "order",
+  "hypervolume",
+  "reference"
+]

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 mark it read-only, non-destructive, and closed-world; the description adds the key behavioral fact that ranking is by Pareto non-domination rather than a single score. It does not discuss tie-breaking, duplicate handling, or filtering behavior beyond what the schema covers, but the core ranking semantics are clear.

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?

A single front-loaded sentence introduces the outcome ('Which candidates...') before naming the Pareto sorting method. It is dense but clear, with no redundant text.

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 an 11-parameter analytical tool with a nested subject object and a full output schema, the description explains the core operation but says little about how fronts are ordered or how filters and labels affect results. Because the schema and output schema are rich, this is largely adequate, though the missing usage guidance is a gap.

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 11 parameters, including maximize/minimize, order, filter, and reference, are already documented in the schema. The description mentions that columns are maximized or minimized but adds no syntax, defaults, or interaction detail beyond the schema.

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 operation—Pareto/non-dominated sorting—over rows in a table evaluated on multiple objectives, so an agent can identify it as a multi-objective ranking tool. It does not explicitly differentiate itself from neighboring rank_* tools, but the Pareto framing is distinctive enough for a 4.

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?

It implies the tool is for finding candidates not dominated across several objectives, but gives no explicit when-to-use criteria, prerequisites, or named alternatives. An agent must infer applicability from the Pareto concept alone.

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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