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.

Input 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

Name	Required	Description
`q`	Yes	q-value per match, in input order
`fdr`	Yes	FDR of the whole set
`decoys`	Yes	how many decoy matches were scored; the FDR estimate stands on this count
`targets`	Yes	how many target matches were scored
`accepted`	Yes	target matches at or under the q-value threshold
`threshold`	Yes	the q-value cut-off `accepted` was counted at

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedOct 21, 2026

addedOutput schema / properties / decoys

Added value: +{
+  "description": "how many decoy matches were scored; the FDR estimate stands on this count",
+  "type": [
+    "number",
+    "null"
+  ]
+}

addedOutput schema / properties / targets

Added value: +{
+  "description": "how many target matches were scored",
+  "type": [
+    "number",
+    "null"
+  ]
+}

addedOutput schema / properties / threshold

Added value: +{
+  "description": "the q-value cut-off `accepted` was counted at",
+  "type": [
+    "number",
+    "null"
+  ]
+}

changedOutput schema / required

Previous value: -[
-  "q",
-  "fdr",
-  "accepted"
-]New value: +[
+  "q",
+  "fdr",
+  "accepted",
+  "threshold",
+  "targets",
+  "decoys"
+]

First observedOct 20, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Annotations already declare a safe read-only, closed-world operation, so the description is free to add domain behavior: it names the underlying algorithm, notes validation against pyteomics, and states what is produced (per-match q-values, set-level FDR, accepted count). It does not explain how correction, ratio, or formula interact with the reported FDR, which keeps it short of a 5.

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?

A single front-loaded sentence with a colon that separates inputs from outputs; nothing is padding. The trailing clause about return values slightly overlaps the output schema, but it remains compact and readable.

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 nine-parameter tool with nested record objects and an output schema, the description covers the core purpose, the required inputs, and the shape of the result, which is what an agent needs to select and call it. The statistical options are left entirely to the schema, which is acceptable given 100% coverage and an output schema.

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 coverage is 100%, so all nine parameters, including ratio, formula, correction and higherIsBetter, are already documented in the schema. The description adds only the pairing semantics for scores/isDecoy and links threshold to the accepted count, which is baseline-level added meaning rather than supplementary detail.

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 computation (FDR and q-values via target-decoy competition) on a specific resource (peptide-spectrum matches), and names the exact required inputs, which distinguishes it cleanly from scoring siblings like score_spectrum or spectrum readers like read_mgf.

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 when to use it by saying 'give each match's score and whether it hit a decoy,' which tells an agent the tool applies once per-match scores and decoy flags exist. It never states prerequisites, when not to use it, or which sibling to choose for protein-level rather than PSM-level FDR (e.g. bayesian_protein_inference), so guidance is implied rather than explicit.

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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