Genomic Intelligence

fetch_ensembl_sequence

Fetch a gene's reference sequence from Ensembl and store it.

    Returns a handle ({ref, name, length, preview, ...}). Pass the
    `ref` to predict_* tools — the bases stay server-side. For
    expression, use fetch_gene_for_expression instead (it prepares
    the TSS-centred window that model needs).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`gene`	Yes	Gene symbol (e.g. 'TP53') or Ensembl ID.
`species`	No	Species name, e.g. 'human', 'mouse'.	human
`flank_bp`	No	Extra bp added on each side of the gene body.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.4/5.0

Behavior4/5

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

The description discloses that the sequence is stored server-side and that a handle is returned, with the bases staying server-side. This goes beyond the annotations (readOnlyHint=false, openWorldHint=true) by explaining the storage behavior and handle semantics, though it doesn't detail persistence limits or error conditions.

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 brief and front-loaded, with a clear purpose in the first sentence. Additional sentences add usage context and the expression alternative without unnecessary fluff.

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, and the key alternative for expression. Combined with the output schema and full parameter descriptions, it provides sufficient contextual completeness for an agent to select and use 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?

The input schema provides comprehensive descriptions for all three parameters (gene, species, flank_bp), so the description doesn't need to add parameter-level detail. The description doesn't reference parameters beyond the general task, but the schema already covers them, making the baseline 3 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 explicitly states 'Fetch a gene's reference sequence from Ensembl and store it,' naming the verb, resource, and action. It also distinguishes the tool by directing expression use to fetch_gene_for_expression, and clarifies the handle-based workflow.

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?

It gives an explicit alternative: 'For expression, use fetch_gene_for_expression instead,' which is clear guidance on when not to use this tool. It also explains the intended usage pattern with predict_* tools, providing context for when to invoke it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Genomic Intelligence tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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

Need Help?