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jean — Molecular Biology & Genomics

scan_sigma70_promoters

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Find σ70 (housekeeping) promoters in a bacterial sequence and say how strong each one is, using the published La Fleur-Hossain-Salis biophysical model - six interaction motifs (UP element, -35, spacer, extended -10, -10, discriminator) plus the R-loop strength of the first transcribed bases, fitted on 22,132 characterised promoters. THE NUMBER IS A FREE ENERGY AND MORE NEGATIVE IS STRONGER, which is the opposite direction from a score; the transcription rate beside it is the paper's own rate law over that energy. It answers "could RNA polymerase start here", not "does it": nothing here knows about regulators, supercoiling or growth phase. It is E. coli σ70 and says nothing about σ38/σ32/σ54 promoters or about another organism's polymerase.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
_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.
max_dgNoreport only configurations at or below this ΔG_total. UNSET BY DEFAULT and deliberately so: this model publishes no significance cutoff, so the answer is a RANKING of the strongest configurations rather than a set of calls, and inventing a floor here is the exact defect the scanner this replaces had. Set it when you have a threshold of your own. In kcal/mol.
_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.
sequenceNothe DNA to scan - leave unset to use the open record.
region_endNo0-based inclusive end. Unset = the whole sequence. Advanced.
max_resultsNohow many to return, strongest (most negative) first. Advanced.
both_strandsNoscan the reverse strand too. On by default: a cryptic promoter does not care which strand your insert was drawn on, and scanning one is how one gets missed.
region_startNo0-based inclusive start of the region to scan. Unset = the whole sequence; pass the selection's start to scope to it. Advanced.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYeshow many are reported (`max_results`, or fewer if the sequence is short or `max_dg` was set)
offsetYeswhere the scanned stretch starts in the sequence: add it to every position above for sequence coordinates. 0 unless a region was given
promotersYesthe configurations, strongest (most negative) first, each with its six motif sequences, their positions and the per-motif energies that sum to its total
strongestYesthe most negative ΔG_total, or null when the sequence is too short to hold one
strongestDgYesthat hit's ΔG_total on its own, for a threshold downstream, or null when there is no hit

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already cover safety (readOnlyHint=true, destructiveHint=false, openWorldHint=false), so the bar is lower, yet the description adds substantial context: the number is a negative-is-stronger free energy (opposite of a score), the accompanying transcription rate is the paper's own rate law, and outputs form a ranking with no significance cutoff. That is meaningful behavioral disclosure well beyond the structured fields.

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?

One dense but front-loaded paragraph that opens with the action and then layers in the sign convention, the rate law, and scope limits. Every clause carries information, though the single block is long and could be broken up for faster scanning.

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

Completeness5/5

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

Given the model's complexity, the description supplies the model provenance, output-sign semantics, and the biological scope caveats, and an output schema exists to carry the return structure. Nothing an agent needs to call this correctly is missing.

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 itself documents max_dg, both_strands, region_start/end and max_results — baseline 3. The description reinforces one point (results are a ranking, not calls) but does not add syntax or format detail beyond the schema.

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 (find σ70 housekeeping promoters in a bacterial sequence) and names the exact method (La Fleur-Hossain-Salis biophysical model over six interaction motifs plus R-loop strength). An agent can immediately distinguish this from generic motif scanners like scan_motif_sites or scan_intrinsic_terminators.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit boundary for use: it answers 'could RNA polymerase start here', not 'does it', and it knows nothing about regulators, supercoiling or growth phase; it also states it says nothing about σ38/σ32/σ54 or other organisms' polymerases. Strong when/when-not framing, though it does not name alternative sibling tools to route to.

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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