Genomic Intelligence

predict_expression

Read-only

Predict a gene's expression from a TSS-centred window.

    Expression is cell-type-specific, so `description` (cell type /
    assay context, e.g. 'K562 cell line') is REQUIRED — the API
    rejects requests without it.

    The model scores exactly 9,198 bp centred on the TSS (±4,599). Two
    ways to supply that:

    - A sequence of exactly 9,198 bp already centred on the TSS. No
      `tss_index` needed — the midpoint is the only legal TSS.
    - A longer locus, 9,198–500,000 bp, plus `tss_index`: the 0-based
      offset of the TSS into it. The API cuts the window for you
      (sequence[tss_index-4599 : tss_index+4599]) and never scans for a
      TSS itself.

    Anything under 9,198 bp is rejected, here and by the API (422) —
    there is no padding or truncation fallback. `tss_index` is required
    for every other length, because a locus with no offset is
    indistinguishable from a mis-centred window.

    An offset that is merely WRONG (e.g. counted over a wrapped FASTA's
    characters, or against a chromosome coordinate instead of an offset
    into THIS sequence) still succeeds and scores the wrong window —
    verify meta.task_specific_counts.scored_window in the response.

    Easiest paths: fetch_gene_for_expression(gene) returns a
    ready-centred handle, and find_genes_and_predict_expression takes a
    raw region and finds each TSS for you.

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`.
`tss_index`	No	0-based offset of the transcription start site into the sequence, counted in bases (whitespace is ignored). Required unless the sequence is exactly 9,198 bp; must leave 4,599 bp on each side. The API scores only sequence[tss_index-4599 : tss_index+4599] and reports the slice it used as meta.task_specific_counts.scored_window — check it: a wrong-but-in-range offset scores the wrong window silently.
`description`	No	REQUIRED experimental context — cell type / assay / conditions (e.g. 'K562 cell line', 'liver tissue'). Expression is cell-type-specific; the API rejects requests without it.
`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

A5/5.0

Behavior5/5

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

Annotations already indicate read-only and non-destructive behavior, and the description adds substantial behavioral context: the API rejects requests without a description, rejects sequences under 9,198 bp with no padding/truncation fallback, and warns that a wrong-but-in-range offset silently scores the wrong window. It also tells users to verify meta.task_specific_counts.scored_window, which goes well beyond the annotations.

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 detailed but tightly structured with a clear opening, bolded requirements, bullet-pointed input modes, and a formula for window slicing. Every sentence conveys necessary operational information—length limits, rejection behavior, error modes, and alternatives—without fluff.

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 tool's complexity—TSS windowing, length constraints, coordinate offset pitfalls, and multiple input paths—the description is thorough and self-contained. It covers parameter requirements, edge cases, verification steps, and sibling-tool alternatives, so the agent has enough context to invoke it correctly. The presence of an output schema means return-value documentation is not necessary.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although the input schema covers all parameters at 100%, the description adds critical semantics: the exact 9,198 bp window, the two acceptable ways to supply input, the slicing formula sequence[tss_index-4599 : tss_index+4599], and the required condition for tss_index. It also clarifies that sequence_ref comes from acquisition tools and that `description` is mandatory, enriching the schema's otherwise neutral parameter descriptions.

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 opens with 'Predict a gene's expression from a TSS-centred window', a specific verb+resource statement that clearly defines the tool's function. It also differentiates from siblings by naming easier alternatives like fetch_gene_for_expression and find_genes_and_predict_expression.

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?

The description explicitly explains the two valid input modes (exactly 9,198 bp centred vs. longer locus with tss_index), states that `description` is REQUIRED, and specifies the exact length constraints. It also points users to easier paths and clarifies when tss_index is required, providing both when-to-use and alternative guidance.

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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