jean — Molecular Biology & Genomics

codon_optimality

Read-only

Frequency of optimal codons (Fop, Ikemura 1981) for an in-frame CDS against a real codon-usage table: what share of its codons are the ones a highly-expressed gene in that host would use. Where CAI scores how CLOSE each codon is to its family's best, Fop scores only whether it IS one of the best, which makes it comparable across genes of different amino-acid composition. Name a table this project holds in table, or one of the bundled hosts in host.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`host`	No	one of the three codon-usage tables bundled with the app. Only applies when table is not given.	E. coli K-12
`table`	No	a codon-usage table this project holds, BY NAME, as the Codon Usage panel lists it — any organism. Takes precedence over `host`. This is the way to pass a table you fetched or built; `host` is only the three tables bundled with the app.
`_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.
`_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.
`sequence`	No	the coding sequence, in frame (a length that is not a multiple of 3 is refused with the reason). Leave it empty to score the open record.
`threshold`	No	Ikemura's optimality cutoff: a codon counts as optimal at this share of its family's most-used codon. 0.8 is the convention, not a measurement, and the right value is host-dependent.

Output Schema

TableJSON Schema

Name	Required	Description
`fop`	Yes	frequency of optimal codons, 0-1
`report`	Yes	the full readout, including which host table answered
`scored`	Yes	codons that carried a call — stops and unevidenced families are excluded from both halves
`nonOptimal`	Yes	how many codons are NOT optimal (the honest denominator beside fop)
`optimalCodons`	Yes	size of the optimal set the reference defines

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 20, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare a safe read operation (readOnlyHint=true, destructiveHint=false), so the bar is lower. The description still adds behavioral context: that table takes precedence over host, that host is limited to three bundled tables, and that the threshold convention is host-dependent and not a fixed measurement.

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 definition, then a comparison clause, then the parameter-selection sentence. Every sentence carries information, though the CAI comparison is slightly elaborate for a tool that needs to be picked quickly.

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 annotations covering the safety profile and an output schema covering return values, the description only needs to establish purpose, metric semantics, and input selection — all present. Nothing an agent needs to call it correctly is missing.

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 both the threshold semantics and the table/host precedence are already spelled out in the schema. The description restates the table/host choice but adds no syntax or format detail beyond structured fields, so the 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 metric (Fop, Ikemura 1981), the exact input (an in-frame CDS) and the reference (a real codon-usage table). It explicitly distinguishes itself from CAI by contrasting 'how CLOSE' vs 'whether it IS one of the best', so an agent can tell it apart from the codon-usage 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?

Conveys the condition that favors Fop over CAI (comparability across genes of differing amino-acid composition) and explains how to select a table via `table` vs the bundled `host` with precedence. It stops short of naming an alternative tool or stating explicit exclusions, but the selection context is clear.

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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