Genomic Intelligence

predict_enhancer

Read-only

Predict enhancer activity (G0 DeepSTARR). 50–500,000 bp.

    50 bp is the task's admission floor (the API 422s below it), not a
    statement about what the model reads: enhancer models score a 249 bp
    context window, so 50–248 bp is accepted and scored against a padded
    window. For a meaningful call, submit at least the 249 bp context.

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

A4.1/5.0

Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses non-obvious behavioral details: the API 422s below 50 bp, the model scoring against a padded 249 bp context window, and that shorter sequences are still accepted. This is valuable context that significantly aids correct invocation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded with the core purpose, followed by one focused paragraph of necessary technical caveats. No sentence is wasted; the formatting emphasizes the critical 249 bp context.

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?

For a four-parameter tool with 100% schema coverage and an output schema, the description provides the essential non-obvious context about sequence length limits and scoring behavior. It does not discuss alternatives or output semantics, but those are largely covered by schema, annotations, and the tool's clear purpose.

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 parameters are already documented. The description adds important meaning for the sequence parameter by explaining the admission floor and context window, which is not present in the schema. Other parameters like model and sequence_ref are left to the schema, but the added length guidance justifies a score above baseline.

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 states the tool predicts enhancer activity using G0 DeepSTARR, giving a specific verb and resource. It does not explicitly differentiate from sibling prediction tools like predict_promoter or predict_chromatin, but the enhancer focus is clear enough.

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?

The description gives concrete guidance on sequence length: the 50 bp admission floor, the 249 bp context window, and the recommendation to submit at least 249 bp for meaningful results. However, it does not explicitly say when to use this tool versus the sibling tools or mention alternatives.

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.

Resources

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