Genomic Intelligence

fetch_gene_for_expression

Fetch a gene's sequence prepared for expression prediction.

    Resolves the gene's TSS via Ensembl and returns the exact
    TSS-centred 9,198 bp window the expression model scores, as a handle
    to pass to predict_expression(sequence_ref=...). Because the window is
    exactly 9,198 bp, no `tss_index` is needed on that call.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`gene`	Yes	Gene symbol (e.g. 'HBB').
`species`	No	Species name.	human

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.2/5.0

Behavior4/5

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

Annotations provide limited safety traits (readOnlyHint=false, openWorldHint=true), so the description carries much of the transparency load. It adds useful behavioral detail: resolves TSS via Ensembl, returns exactly 9,198 bp, and clarifies that tss_index is unnecessary. This goes beyond basic annotations, though it does not mention potential side effects or failure modes.

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 concise: two sentences that front-load the purpose, then provide the key detail about the window size and the handle usage. No word is wasted, and the structure is logical.

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?

The description is highly complete for a tool with an output schema: it explains the return value's role and how to use it in a downstream call. It could mention edge cases (e.g., gene not found, species restrictions) but is otherwise sufficient for an agent to invoke the tool 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 description coverage is 100%, so the schema already documents 'gene' and 'species' with clear descriptions. The description adds meaning by explaining how the gene is used (TSS resolution) but does not introduce new parameter-specific details. Baseline 3 is appropriate given high schema coverage.

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's action: fetching a gene's sequence prepared for expression prediction. It distinguishes the tool from siblings by specifying the exact TSS-centred 9,198 bp window and its role as a handle for predict_expression, making it unique among fetch and prediction tools.

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 clear context: this tool should be used when preparing a gene sequence for expression prediction, and it explicitly mentions the integration point with predict_expression. However, it does not explicitly state when to avoid this tool or compare it with alternatives like fetch_ensembl_sequence or find_genes_and_predict_expression.

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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