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protein_disorder

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Intrinsic disorder along a protein by FoldIndex (Prilusky 2005): I_F = 2.785·⟨H⟩ − |⟨R⟩| − 1.151 over a sliding window, where ⟨H⟩ is mean normalized Kyte-Doolittle hydropathy and ⟨R⟩ the mean net charge per residue.

Input Schema

NameRequiredDescriptionDefault
pHNopH for the Henderson-Hasselbalch model, ignored by 'net' (default 7.0, the pH the paper's ⟨R⟩ is defined at). Only applies when chargeModel is henderson-hasselbalch.
_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.
pKaSetNonamed pKa set for the Henderson-Hasselbalch model (default IPC_protein, which is idpr's). Values: EMBOSS (EMBOSS (Rice 2000)), DTASelect (DTASelect (Tabb 2002)), Solomons (Solomons (Solomons 1992)), Sillero (Sillero (Sillero 1989)), Rodwell (Rodwell (Rodwell 1982)), Lehninger (Lehninger (Nelson 2017)), Toseland (Toseland (Toseland 2006)), Thurlkill (Thurlkill (Thurlkill 2006)), Nozaki (Nozaki (Nozaki 1971)), Dawson (Dawson (Dawson 1989)), Bjellqvist (Bjellqvist (Bjellqvist 1994)), ProMoST (ProMoST (Halligan 2004)), IPC_protein (IPC_protein (Kozlowski 2016)), IPC_peptide (IPC_peptide (Kozlowski 2016)), Vollhardt (Vollhardt (Vollhardt 2014)).IPC_protein
windowNosliding window in residues, odd so a window has a centre residue to report at (default 51, the size the FoldIndex paper uses). Short windows expose single loops; long ones report whole domains.
proteinYesthe protein sequence (unambiguous one-letter codes); wire it from translate_sequence.
_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.
chargeModelNo'net' counts whole charges K+R−D−E, which is what the paper does; 'henderson-hasselbalch' uses fractional charges at `pH`. They answer different questions and only the first is settled by the paper.net
countHistidineNocount histidine as positive in the 'net' model. False by default: the paper says charged residues at pH 7.0, and His (pKa ~6) is ~9% protonated there, so Uversky's set is K,R vs D,E — exposed because some FoldIndex descendants do count it.

Output Schema

NameRequiredDescriptionDefault
plotYesthe FoldIndex profile as a plot; below zero is predicted disordered
scoresYesjust the I_F value per window, parallel to `positions`
windowYesthe window actually used
overallYeswhole-protein FoldIndex from the global means — the number the 2005 paper's abstract reports. Below zero = predicted unfolded
windowsYesevery window: { position, centerResidue, hydropathy, charge, score } — the series a plot draws
boundaryYesthe Uversky 2000 boundary |⟨R⟩| = 2.785·⟨H⟩ − 1.151 evaluated at this protein's ⟨H⟩ — what `meanNetCharge` is above or below
segmentsYesruns of consecutive negative windows as 1-based inclusive residue spans, with each run's mean and minimum I_F
unfoldedYeswhether the whole-protein point sits above that boundary, i.e. predicted natively unfolded
positionsYes1-based centre residue of each window, parallel to `scores`
pKaSetNameYesthe pKa set actually used, or '' for the 'net' model which needs none
chargeModelYesthe charge model actually used
windowCountYeshow many windows were scored — the series length, so a fraction can be read back
meanNetChargeYes|⟨R⟩| over the whole chain — the y of the Uversky point
meanHydropathyYes⟨H⟩ over the whole chain — the x of the Uversky charge-hydropathy point
disorderedWindowsYeshow many windows score below zero
disorderedFractionYesthat count as a fraction of all windows

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior4/5

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

The annotations already declare this as a safe, read-only, non-destructive operation. The description adds genuine algorithmic transparency beyond those annotations by giving the FoldIndex formula, the sliding-window concept, and the hydropathy/charge components. It does not discuss output interpretation or method limitations, but the output schema covers return values.

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 definition is a single dense sentence that front-loads the method and formula with no filler. It is efficient for a scientific calculation tool, though the formula-heavy phrasing could be slightly more scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 8-parameter tool with nested objects and an output schema, the description covers the core algorithm but omits usage context and explicit routing versus the sibling protein_disorder_metapredict. The rich schema and output schema compensate substantially, leaving the description adequate but incomplete.

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%, and the parameter documentation is already very detailed. The description reinforces the role of the sliding window and mean net charge in the formula, but it does not add meaning beyond what the schema already provides for parameters such as pH, pKaSet, and countHistidine, so it merits the baseline 3.

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 clearly identifies the resource and method: intrinsic disorder prediction along a protein using FoldIndex. It is specific enough for an agent to know what the tool computes, but it does not explicitly distinguish this from the sibling protein_disorder_metapredict, so it falls short of a 5.

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?

There is no explicit when-to-use guidance, no mention of alternatives, and no prerequisites. The method name implies that an agent should choose this when FoldIndex is desired, but that inference is left entirely to the reader rather than stated.

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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