jean — Molecular Biology & Genomics

target_decoy_qvalues

Read-only

False discovery rate and q-values for peptide-spectrum matches by target-decoy competition (validated against pyteomics): give each match's score and whether it hit a decoy; returns every match's q-value, the whole set's FDR, and how many targets pass a q-value threshold. Supports the +1 and finite-sample corrections of Levitsky et al. 2017.

Input Schema

TableJSON Schema

Name	Required	Description
`ratio`	No	decoy-to-target database size ratio.
`_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.
`scores`	Yes	one score per match.
`formula`	No	1: decoys / targets. 2: (decoys + estimated false targets) / all matches.
`isDecoy`	Yes	one flag per match: true when it is a decoy hit.
`_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.
`threshold`	No	q-value cut-off for the accepted count.
`correction`	No	0 none; 1 the +1 correction; 2 finite-sample expectation; a value in (0, 1) is a confidence level.
`higherIsBetter`	No	true for scores like HyperScore; false for e-values and PEPs.

Output Schema

TableJSON Schema

Name	Required	Description
`q`	Yes	q-value per match, in input order
`fdr`	Yes	FDR of the whole set
`accepted`	Yes	target matches at or under the q-value threshold

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 20, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already establish it is a safe, read-only, closed-world computation. The description goes beyond them by disclosing the validation source (pyteomics), the supported statistical corrections (Levitsky et al. +1 and finite-sample), and what the result contains. Good added context without contradicting annotations.

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?

Two tight sentences, front-loaded with the core purpose before the return shape and correction details. No filler; every clause conveys distinct information.

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 a 9-parameter tool with an output schema, the description covers purpose, inputs, outputs, and the statistical options. Since an output schema exists, the return-value sentence is somewhat redundant, but nothing critical 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%, so parameters are already documented in the schema; baseline is 3. The description echoes the score/isDecoy inputs and threshold notion but adds no syntax or semantics beyond the schema's own param descriptions.

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+resource: computes false discovery rate and q-values for peptide-spectrum matches via target-decoy competition. It is clearly distinguishable from siblings like score_spectrum or infer_proteins, and cites a concrete implementation reference (pyteomics).

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?

The description implies the calling context (you have per-match scores and decoy flags) but never explicitly states when to use this tool versus alternatives, nor any prerequisites. Usage is inferable but not spelled out.

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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