Genomic Intelligence

predict_promoter

Read-only

Predict promoter regions (G0). 300–500,000 bp.

    Returns the {data, meta} envelope: data.regions lists predicted
    promoters with start/end/score.

    300 bp is the task floor for every promoter model. The default
    g0-promoter-2000bp scans a 2,000 bp context window, so a shorter
    (but ≥300 bp) sequence is still scored — against a window padded out
    to that size. Check the chosen model's bio_spec.context_window_bp via
    list_models to know whether it saw real sequence or padding.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`model`	No	Optional model id; omit for the task default. See list_models.
`sequence`	No	DNA bases A/C/G/T/N (case-insensitive). Line breaks are ignored (a wrapped FASTA body may be pasted verbatim; a `>` header line may not). Mutually exclusive with `sequence_ref`.
`sequence_ref`	No	Handle (seq_…) from any acquisition tool (fetch_ensembl_sequence, fetch_region, fetch_gene_for_expression, load_demo_sequence, load_local_fasta, store_inline_sequence). Mutually exclusive with `sequence`.
`sequence_name`	No	Label echoed back in the response (ignored when `sequence_ref` is used).	sequence

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.4/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds valuable behavioral detail: it clarifies the return envelope ({data, meta}) and specifies that data.regions contains start/end/score. It also discloses the padding behavior for sequences shorter than the model's context window – a non-obvious side effect that annotations do not cover. This goes beyond the structured metadata.

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 description is compact (about 100 words) and front-loaded with the core purpose. It uses a clear structure: purpose statement, return envelope, and a practical caveat about context windows. Each sentence adds value, though the final sentence about checking model context could be seen as slightly verbose but is still actionable.

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 output schema is present, the description needn't detail return types, but it goes further by explaining the data.regions structure and the padding behavior, which are crucial for correct interpretation. It also references list_models for model-specific details, making the tool self-sufficient. For a predictive tool with complex input constraints, the description provides enough context to use it 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?

The input schema has 100% parameter description coverage, with each parameter (model, sequence, sequence_ref, sequence_name) already described in detail, including mutual exclusivity and ignored cases. The description adds no additional parameter-specific meaning; it focuses on output and constraints. Thus a baseline score of 3 is appropriate because the schema carries the explanatory load.

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?

The description clearly states the tool predicts promoter regions (G0) with a size range (300–500,000 bp). It distinguishes from sibling tools like predict_enhancer and predict_chromatin by specifying the exact biological target and provides a unique constraint on input length. The verb-resource pairing is specific and unambiguous.

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?

The description provides concrete guidance on input length requirements (300 bp floor) and explains how the model handles shorter sequences via context window padding. It directs the agent to check model specifics via list_models, which is practical. However, it does not explicitly contrast with alternative predictors (e.g., when to choose predict_enhancer over this), so it lacks explicit exclusionary guidance but still offers strong contextual direction.

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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TDQS

A4.4/5.0

Disambiguation5/5

Each tool targets a distinct operation: fetching gene/region/expression-window sequences, finding genes, predicting chromatin/enhancer/expression/promoter/splice, and managing jobs/models. The fetch tools are explicitly cross-referenced with guidance on which to use, eliminating ambiguity.

Naming Consistency5/5

All tools follow a consistent lowercase snake_case verb_noun pattern, with clear verbs like fetch, find, predict, get, list, load, and store. Even compound names like find_genes_and_predict_expression remain readable and predictable.

Tool Count5/5

Fifteen tools is at the upper end of the ideal range but each tool earns its place: five prediction tasks, five sequence acquisition paths, two gene-finding tools, plus job and model management. No tool feels redundant or superfluous.

Completeness4/5

The surface covers sequence acquisition, gene finding, five prediction tasks, async job polling, and model discovery—strong coverage for the stated domain. A minor gap is that store_inline_sequence references a load_local_fasta tool that does not exist in the tool list, though agents can work around it via fetch_ensembl_sequence or fetch_region.

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