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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.

Input Schema

NameRequiredDescriptionDefault
hostNoone of the three codon-usage tables bundled with the app. Only applies when table is not given.E. coli K-12
tableNoa 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.
_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.
_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.
sequenceNothe 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.
thresholdNoIkemura'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

NameRequiredDescriptionDefault
fopYesfrequency of optimal codons, 0-1
reportYesthe full readout, including which host table answered
scoredYescodons that carried a call — stops and unevidenced families are excluded from both halves
nonOptimalYeshow many codons are NOT optimal (the honest denominator beside fop)
optimalCodonsYessize of the optimal set the reference defines

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=false, and destructiveHint=false, so the safety profile is covered. The description adds the metric definition and table framing, but does not describe input-precedence behavior, error handling, or response characteristics beyond what the schema and output schema provide.

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?

The description is a single, front-loaded sentence that defines the metric and its interpretation without repetition or filler. Every phrase contributes to understanding what is being computed.

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?

With high schema coverage, an output schema, and read-only annotations, the description is largely complete for calling the tool correctly. It could better distinguish this from sibling codon-usage tools, but the core computation and input context are adequately stated.

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 parameters are already well documented, including host/table precedence, in-frame sequence handling, and threshold meaning. The description reinforces 'in-frame CDS' and 'real codon-usage table' but adds little new parameter semantics beyond the schema, so the baseline of 3 is appropriate.

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?

Names the specific metric (Fop, Ikemura 1981), the input (in-frame CDS), and the reference (real codon-usage table), then defines the result in plain terms. It does not distinguish this from sibling tools such as codon_usage_analysis or codon_usage_distance, so it is clear but not sibling-differentiated.

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

Usage Guidelines2/5

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

The description implies the tool is for scoring an in-frame CDS against a codon-usage table, but it gives no explicit when-to-use guidance, no exclusions, and no named alternatives. An agent must infer when to choose this over related codon-usage tools.

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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