Genomic Intelligence

load_demo_sequence

Load a bundled demo reference sequence and return a handle.

    The server ships one curated, task-correct positive control per task
    (list them via the gi://sequences resource) — e.g.
    `expression_hbb_k562` is a ready-to-use K562 expression window for
    predict_expression. Stores the demo and returns a handle to pass to a
    predict_* tool: no Ensembl fetch, no quota. Handy for smoke-testing a
    prediction end-to-end.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	Yes	Demo name from gi://sequences, e.g. 'expression_hbb_k562', 'promoter_tp53', or 'annotation_hbb_chr11'. A gene token like 'TP53' also resolves.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.1/5.0

Behavior4/5

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

With no helpful annotations (all false), the description carries the transparency burden. It discloses that the tool 'Stores the demo and returns a handle', clarifies 'no Ensembl fetch, no quota', and explains that it provides a curated positive control. These are meaningful behavioral traits beyond the schema, though it does not cover failure modes or persistence details.

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 front-loaded with the core purpose, followed by valuable context and an example. Its length is justified by the need to explain the 'bundled positive control' concept, though it is slightly more verbose than strictly necessary.

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 single-parameter tool with an output schema, the description fully explains when and why to use it, and how the demo relates to prediction tools. It lacks only explicit error-case or edge-case behavior, which is not critical given the simplicitly and output schema.

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 coverage is 100% and the schema already includes examples and the gi://sequences resource. The description also provides an example and references the resource, but it does not add new semantics beyond what the schema already states, so the baseline score of 3 is appropriate.

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 opening sentence 'Load a bundled demo reference sequence and return a handle' uses a specific verb and resource, clearly distinguishing it from fetchers like fetch_ensembl_sequence. The phrase 'no Ensembl fetch' further differentiates it as a local, pre-bundled option.

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 is for smoke-testing a prediction end-to-end and avoids Ensembl fetch/quota, implying use for quick local tests. However, it does not explicitly name alternatives or give 'when not to use' guidance, so it stops short of a 5.

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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