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jean — Molecular Biology & Genomics

crispr_score_deephf

Read-only

Score CRISPR guides with DeepHF: a predicted knockout EFFICIENCY between 0 and 1 for wild-type SpCas9 (U6 or T7 promoter), eSpCas9(1.1) or SpCas9-HF1. It is not a z-score and not comparable with Rule Set 3 — the two answer different questions and must never be averaged or ranked together. Takes 23-nt protospacers (20-nt spacer + NGG PAM), which is what crispr_design_guides produces; a guide whose PAM is not canonical NGG comes back null rather than extrapolated, because the model was trained on NGG only. THE WEIGHTS DOWNLOAD ON FIRST USE, 3-5 MB per model, and scoring is slow by construction: each guide is folded twice, once with the 79-nt tracrRNA scaffold, before the network sees it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNowhich published model to score under. This is a scientific choice about YOUR experiment — which Cas9 variant, and which promoter the sgRNA is expressed from — not a quality setting, and scores from two models are not comparable. Values: wt_u6 (WT SpCas9 · U6 promoter), wt_t7 (WT SpCas9 · T7 promoter), wt (WT SpCas9 · both promoters), esp (eSpCas9(1.1)), hf (SpCas9-HF1).wt_u6
_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.
protospacersYesthe 23-nt protospacers to score — 20-nt spacer plus the 3-nt PAM, exactly as DeepHF takes them. Anything that is not 23 nt of unambiguous A/C/G/T ending in GG scores null rather than being silently trimmed or extrapolated.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
bestYesthe highest-scoring protospacer, or empty when none could be scored
modelYeswhich model produced these numbers. Scores from two models are not comparable
scoredYeshow many of the inputs DeepHF is defined on — the accounting, not an assumption
scoresYesevery protospacer with its efficiency, IN THE ORDER GIVEN — null where DeepHF is not defined, with the reason beside it
efficienciesYesjust the numbers, same order — null-preserving, so it lines up with the input

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare a safe read-only, closed-world operation, and the description adds substantial behavior beyond them: first-use weight downloads of 3-5 MB per model, slow scoring because each guide is folded twice with a 79-nt tracrRNA scaffold, and null returns rather than extrapolation for non-NGG PAMs. These are operational facts an agent needs to plan around.

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?

Front-loaded with the core verb and output semantics, then constraints and costs in descending priority; every sentence carries information. The all-caps warning about weight downloads is a little shouty and slightly interrupts the flow, keeping it out of the top score.

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?

An output schema exists so return values need not be explained, yet the description still clarifies the null case and score scale. With model semantics, input format, runtime and download caveats all covered, an agent has everything needed to call this correctly.

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 coverage is 100% and the `model` enum already carries the 'scientific choice, not a quality setting' framing, so the description largely restates it. The added nuance that scores across models are not comparable is useful but already implied by the schema text, so this sits at the baseline 3.

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), resource (CRISPR guides), the method (DeepHF) and the exact output semantics (predicted knockout efficiency between 0 and 1). It explicitly separates itself from Rule Set 3 scoring, so an agent can distinguish it from sibling scoring tools without opening any schema.

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

Usage Guidelines5/5

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

Tells the agent what input to supply (23-nt protospacers as produced by crispr_design_guides), which models apply to which Cas9 variant/promoter, and gives an explicit when-not: scores from different models and from Rule Set 3 must never be averaged or ranked together. Non-canonical PAM handling is also specified.

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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