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jean — Molecular Biology & Genomics

alignment_agreement

Read-only

Read an alignment window and say how well its reads agree with the reference underneath them, the way the pileup's own bar does: the compared and differing base counts, plus the RefSeq gene models and known dbSNP variants over the same window. The reference is a FASTA in this project or a public UCSC assembly, exactly as the panel offers it. There is no threshold and no verdict: a rate in the tens of percent almost always means the reads were aligned to a different assembly than the one asked for, and when the file declares its own reference length the answer says so as a fact rather than as a judgement.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
locusNothe window, as a locus string (`chrM:3,300-3,460`). Unset, it is the whole of the alignment's first reference — the window the panel opens on.
_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.
variantsNoalso report the known dbSNP variants in the window. Off by default, as in the panel: it is a third layer over the reads and a dense window can hold thousands.
alignmentNowhich BAM or SAM in this project, by name. Unset, it is the project's only alignment — and it refuses rather than guessing when there is more than one.
referenceNowhat to compare against: a FASTA in this project by name, or a UCSC assembly id (`hg38`, `hg19`, `mm39` — the same list the panel's picker fetches). Unset is the panel's own default, No reference, and then only the window and its annotation are reported.
gene_trackNowhich NCBI RefSeq set the gene models come from. Only meaningful against a UCSC assembly. The default is one curated transcript per protein-coding gene, which carries no mitochondrial genes at all — RefSeq Curated does. Values: ncbiRefSeqSelect (RefSeq Select and MANE), ncbiRefSeqCurated (RefSeq Curated), ncbiRefSeq (RefSeq All), ncbiRefSeqPredicted (RefSeq Predicted).ncbiRefSeqSelect

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rateYesdiffering aligned bases as a fraction of compared ones, 0 when nothing matched up
genesYesthe RefSeq models overlapping the window: symbol, accession, strand and bounds, as UCSC states them
readsYesreads read for the window
noCallYesaligned bases where one side is N — counted as neither, which is where this differs from an aligner's NM
windowYesthe window actually measured, as a locus string
cautionsYeswhat this run earned: a reference whose length disagrees with the one the file declares, no-call bases counted as neither, and the window the numbers describe
comparedYesaligned bases that had a reference base to compare against — the denominator
variantsYesthe dbSNP records in the window when asked for: rsID, alleles, class, highest reported frequency and ClinVar significance
conclusionYesthe reading in one sentence, generated from this run — the panel's own words
mismatchedYesof those, how many differ
pathogenicYeshow many of those ClinVar calls pathogenic

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare a safe read-only, non-open-world profile, so the bar is lower, and the description still adds real behavior: no threshold and no verdict, refusal rather than guessing when multiple alignments exist, the dense-window warning for dbSNP variants, and the interpretation of high mismatch rates as a wrong-assembly signal. That is meaningful context beyond the annotations, though permission/authentication needs and return shape are left to the output schema.

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?

The purpose is front-loaded in the first clause, and the remaining sentences mostly earn their place by explaining defaults and the meaning of a mismatch rate. The prose is dense and meandering in places (the 'exactly as the panel offers it' and 'as a fact rather than as a judgement' phrasings), so it is not maximally tight, but nothing is gratuitous.

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 explanation, and annotations carry the safety profile; the description covers the remaining behavioral burden well (defaults, refusal semantics, interpretation guidance, variant-density caveat). Only the relationship to sibling tools is genuinely missing, which keeps it short of a 5.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so a 3 would be the baseline, but the description adds interpretive meaning the schema lacks: variants is off by default because it is 'a third layer' that can hold thousands of rows, and gene_track's default (RefSeq Select) 'carries no mitochondrial genes at all — RefSeq Curated does.' The reference default of 'No reference' and the window default are also restated with intent.

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?

The description opens with a specific verb+resource: 'Read an alignment window and say how well its reads agree with the reference underneath them,' and even names the concrete outputs (compared and differing base counts, gene models, dbSNP variants). It is clear what the tool produces, but it never names or contrasts a sibling like alignment_window or compare_reads_to_reference, which also operate over alignment windows, so sibling differentiation rests on inference.

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?

Usage context is implied rather than stated: the description explains defaults (unset alignment, 'No reference') and warns that a tens-of-percent rate usually means a wrong assembly, which hints at when the tool is diagnostic. However, it never says when to choose this over alternatives such as alignment_window or compare_reads_to_reference, so an agent must infer the selection rule.

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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