jean — Molecular Biology & Genomics

score_spectrum

Read-only

Score how well an MS/MS spectrum matches a peptide with the X!Tandem HyperScore (validated against OpenMS): b and y ions are generated for the peptide, matched to the observed peaks within a tolerance, and scored as ln(1 + dot product) + ln(matched b!) + ln(matched y!).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`mz`	Yes	observed peak m/z values.
`_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.
`peptide`	Yes	candidate peptide, one-letter residues with optional ProForma tags (a signed mass, a Unimod name, or UNIMOD:accession).
`_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.
`intensity`	Yes	observed peak intensities, one per m/z.
`tolerance`	No	fragment match tolerance.
`toleranceUnit`	No	unit of the tolerance.	Da
`precursorCharge`	No	precursor charge; fragments are generated at charges 1 to max(1, charge - 1).

Output Schema

TableJSON Schema

Name	Required	Description
`score`	Yes	HyperScore (natural log scale; higher is a better match)
`detail`	Yes	matched b and y ion counts, mean match error, theoretical peak count
`matches`	Yes	one row per matched ion: ion name, theoretical and observed m/z, intensity, error in the tolerance unit

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 20, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already declare read-only, non-destructive, closed-world behavior, so the bar is lower. The description adds valuable algorithmic transparency: b/y ion generation, tolerance-based matching, and the exact scoring formula. It does not mention auth or rate limits, but for a pure read/score operation this is rich context.

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?

One front-loaded sentence with no padding. The algorithm formula is dense but necessary for understanding the score, and the sentence is appropriately sized for the tool's complexity.

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?

An output schema exists, so return values need no description, and annotations cover safety. The description supplies the algorithm context needed to interpret the score. It could optionally note score interpretation or input validation, but overall it is complete for a computational tool.

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 schema already documents all eight parameters including defaults and enums. The description mentions peptide, tolerance, and peak matching but adds no syntax or format details beyond what the schema provides. Baseline 3 is appropriate.

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 (Score) and resource (MS/MS spectrum match to a peptide) with the named X!Tandem HyperScore algorithm. It distinguishes itself from siblings like peptide_mass or fragment_ions by describing the scoring mechanism and validation against OpenMS.

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 explains how scoring works but never states when to use this tool, when not to, or names any alternative. An agent must infer applicability from the tool name and algorithm details alone.

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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