Genomic Intelligence

find_genes_and_predict_expression

Read-only

Find genes in a sequence, then predict each gene's expression (composite).

    Server-side chaining in ONE call: finds genes (transcript intervals,
    with their TSS) in the sequence, then predicts expression off each
    discovered TSS in the given experimental context. This is the right
    tool whenever you want expression for a raw region or sequence — e.g.
    "find the genes in chr8:… and predict their expression in K562".
    predict_expression scores ONE TSS window and needs you to know where
    that TSS is (either a pre-centred 9,198 bp window or a `tss_index`);
    this tool discovers every gene's TSS itself. It has no 9,198 bp floor
    and no tss_index; it starts with gene finding, so it takes
    1,000–500,000 bp.

    Runs async internally at every size (the annotate stage is slow even
    for small inputs), so progress always streams. With wait=True
    (default), blocks and streams progress, then returns the result
    {data, meta} — it never returns a job_id on this path. With wait=False
    (detached), returns {data: {job_id, status: 'submitted'}} immediately —
    poll it with get_job. Because it ends in expression, `description`
    (cell type / assay context) is REQUIRED.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`wait`	No	Default True: block and stream progress until the result is ready. Set False for detached mode — returns a job_id immediately to poll with get_job.
`sequence`	No	DNA bases, 1,000-500,000 bp (line breaks ignored). Mutually exclusive with sequence_ref.
`description`	No	REQUIRED experimental context — cell type / assay / conditions (e.g. 'K562 cell line'), applied to every found gene. The workflow ends in expression, which the API rejects without it.
`sequence_ref`	No	Stored sequence handle. Mutually exclusive with sequence.
`sequence_name`	No	Label echoed back.	sequence

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.9/5.0

Behavior5/5

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

The description goes well beyond the readOnlyHint/openWorldHint annotations by revealing async behavior, progress streaming, blocked vs. detached return shapes, the absence of a job_id on the blocking path, polling behavior on the detached path, and the hard requirement for expression-oriented description. No annotation contradiction exists.

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 longer than average but every sentence earns its place: first sentence states the purpose, then it scopes usage, differentiates from sibling tools, covers async modes, return shapes, and a required parameter. It is well structured, front-loaded, and contains no tautology or filler.

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—server-side chaining, dual wait modes, async behavior, required context, and comparison to siblings—the description fully equips an agent to select and invoke it correctly. Output schema exists, so return values need no further explanation.

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 descriptions already cover all 5 parameters with 100% coverage, so the baseline is 3. The description adds useful context beyond the schema: explanation of why description is required, the 1,000–500,000 bp length constraint in operational terms, and clarification of wait modes. This justifies a 4 rather than a 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?

The description opens with a specific verb+resource: 'Find genes in a sequence, then predict each gene's expression (composite).' It clearly states server-side chaining and explicitly contrasts itself with the sibling predict_expression tool, making its scope and distinct value unambiguous.

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 says when to use this tool ('whenever you want expression for a raw region or sequence'), contrasts it with predict_expression ('scores ONE TSS window and needs you to know where that TSS is'), and gives practical operational guidance including sequence length range, wait=True/False modes, polling with get_job, and the required description parameter.

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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