Genomic Intelligence

fetch_region

Fetch a genomic region by coordinates from Ensembl and store it.

    For "find the genes in chr8:127,680,000-127,800,000"-style requests:
    resolves a coordinate range to reference sequence and returns a handle
    ({ref, name, length, ...}) to pass to find_genes / predict_* — the bases
    stay server-side. Plus strand by default, which is what the gene-finder
    expects. For a gene by name use fetch_ensembl_sequence; for expression
    use fetch_gene_for_expression.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`region`	Yes	Genomic coordinates, e.g. 'chr8:127,680,000-127,800,000'. Commas, en/em dashes and '..' are accepted; the 'chr' prefix is optional.
`strand`	No	1 = plus (default), -1 = minus. find_genes (gene finding) is plus-oriented — keep 1 for annotation; use -1 only for a strand-sensitive task on a known minus-strand locus.
`species`	No	Species name, e.g. 'human', 'mouse'.	human
`flank_bp`	No	Extra bp added on each side of the region.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.3/5.0

Behavior4/5

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

The description explains the tool's storage behavior ('returns a handle ... the bases stay server-side') and the default strand expectation, which adds context beyond the annotations. It does not fully describe the lifecycle of the stored handle or potential side effects, but the 'store it' wording aligns with readOnlyHint=false. No contradiction with annotations.

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 purpose and includes a usage example, return-behavior, strand default, and alternatives. It is slightly wordy in the middle sentence but every sentence contributes useful information. It earns a 4, not a 5, due to minor run-on structure.

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 covers the core functionality, return handle, strand expectation, and sibling distinctions. An output schema exists, so return values need not be detailed. The description is complete enough for an agent to understand when and how to use the tool, with no significant gaps given the tool's moderate complexity.

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 parameter semantics are already fully specified in the schema. The description adds marginal value by clarifying that the plus strand is expected by find_genes, but it does not elaborate on the other parameters beyond what the schema provides. Baseline 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 description uses a specific verb ('Fetch a genomic region by coordinates from Ensembl and store it') and identifies the resource (genomic region by coordinates). It also explicitly distinguishes from siblings by directing users to fetch_ensembl_sequence for gene-by-name and fetch_gene_for_expression for expression requests, making the purpose clear and differentiated.

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 states exactly when to use this tool ('For "find the genes in chr8:127,680,000-127,800,000"-style requests') and provides explicit alternatives for other scenarios. This gives the agent clear guidance on tool selection, including a concrete example and exclusions.

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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