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biocontext-ai

nucleotide_archive_mcp

generate_download_script

Create an executable bash script to download all data files from a study accession, with built-in MD5 verification.

Instructions

Generate executable bash script to download all study data files.

Usage Tips

After identifying interesting studies, generate a download script for the user to execute. Returns script content and optionally saves to file. Script includes MD5 verification commands. Typical workflow: search_rna_studies() → get_study_details() → generate_download_script().

Returns

dict Dictionary containing: - study_accession: Queried study - script_content: Complete bash script ready to execute - file_count: Number of files the script will download - total_size_gb: Total download size in GB - script_path: Save location (if output_path provided) - message: Success message (if saved to file) - error: Error message if any

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
study_accessionYesStudy accession from search results. Accepts SRP/ERP/DRP or PRJNA/PRJEB/PRJDB formats
output_pathNoFile path to save script (e.g., './download.sh'). If None, returns script content without saving. Script will be made executable (chmod 755)
script_typeNoDownload tool to use (wget or curl). wget is recommended for resumable downloads with -nc flagwget
file_formatNoFile format to download (fastq, submitted, or sra). FASTQ is most commonfastq

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries full burden. It discloses that the tool optionally saves a script to file (making it executable with chmod 755) and includes MD5 verification commands. It also details the return dictionary. Missing are explicit mentions of overwrite behavior, permissions, or error handling, but overall it is sufficiently transparent for a script generation tool.

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 well-structured with a clear header, Usage Tips, and Returns section. It is concise but comprehensive, front-loading the purpose. Slightly could be trimmed, but overall efficient.

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?

Given the schema coverage is 100% and an output schema exists (though not shown), the description covers purpose, usage workflow, and return values. It could mention idempotency or specific error conditions, but it is largely complete for a script generation tool.

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%, so baseline is 3. The description does not add new meaning beyond the schema's descriptions for each parameter; it focuses on usage and returns. No additional parameter semantics are provided.

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: 'Generate executable bash script to download all study data files.' This clearly distinguishes it from siblings like get_download_urls (URLs) and search_rna_studies (search).

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 Usage Tips section explicitly states 'After identifying interesting studies, generate a download script' and provides a typical workflow: search_rna_studies() → get_study_details() → generate_download_script(). This gives clear context for when to use it, though it does not explicitly list when not to use it or alternative tools.

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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