Jean by Melad Labs

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

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

A3.7/5.0

Behavior3/5

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

Annotations already declare a read-only, non-destructive, closed-world operation, so the safety profile is covered. The description adds provenance ('validated against pyteomics') and names supported corrections, but those correction details are already enumerated in the 'correction' parameter description, so the added behavioral context is modest.

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?

Two sentences, front-loaded with the purpose before the input/output details. Slightly dense with parenthetical and citation, but every clause carries information and nothing is redundant filler.

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 nested objects and an output schema, this is largely complete: purpose, required inputs, outputs, and the statistical variants are all touched. The only shortfall is that when/why to prefer this over sibling scoring/statistics tools is unstated.

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 the baseline is 3. The description restates the two required inputs (scores, isDecoy) which the schema already documents, and adds no extra meaning for ratio, formula, threshold, correction, or higherIsBetter beyond what the schema provides.

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 and resource: computing FDR and q-values for peptide-spectrum matches via target-decoy competition. This is clearly distinguishable from neighbors like score_spectrum, bayesian_protein_inference, and infer_proteins, and the parenthetical '(validated against pyteomics)' pins the semantics further.

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 tells the agent what inputs to supply ('give each match's score and whether it hit a decoy') and implies the workflow (post-scoring statistical filter), but never explicitly states when to use this versus alternatives such as score_spectrum or bayesian_protein_inference, nor any prerequisites or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Jean by Melad Labs tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?