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by com.seqbench

Fetch sequence by accession

sequence_fetch
Read-onlyIdempotent

Fetch a public DNA/protein record by accession from NCBI Nucleotide, NCBI Protein, UniProt, or Ensembl (e.g. NM_000546, NP_000537, P04637, ENSG00000141510). Only the accession is sent upstream. Use sequence_search first if you only know a gene/organism name, not an accession. For an Ensembl transcript ID this returns spliced cDNA; for a gene ID it returns the full genomic locus (introns included) — Ensembl's own default for each ID type.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dbNoDatabase to query; auto-detects from the accession format.auto
formatNoOutput format (GenBank is only available for NCBI accessions — UniProt and Ensembl are FASTA-only).fasta
accessionYesGenBank/RefSeq accession (e.g. NM_000546), UniProtKB accession (e.g. P04637), or Ensembl stable ID (e.g. ENSG00000141510, ENST00000335137).

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already indicate read-only and idempotent behavior. The description adds valuable behavioral context, such as 'Only the accession is sent upstream' (privacy) and the detailed behavior for Ensembl IDs (spliced cDNA vs. genomic locus). 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, each earning its place: main purpose with examples, privacy note, and usage guidance with Ensembl nuance. Front-loaded and efficiently written without redundancy.

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?

For a tool with 3 parameters and no output schema, the description covers core behavior, databases, usage scenario, and a special case for Ensembl. It is sufficiently complete given the annotations and schema richness.

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 description coverage is 100%, so baseline is 3. The description adds extra meaning beyond the schema by explaining that 'auto' db detects from format and that GenBank format is only available for NCBI. This enhances parameter understanding.

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 clearly states the tool's verb ('Fetch'), resource ('DNA/protein record'), and scope ('by accession from NCBI Nucleotide, NCBI Protein, UniProt, or Ensembl'), with concrete examples. It also distinguishes itself from the sibling 'sequence_search' by specifying the precondition of having an accession.

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 advises to use 'sequence_search' if only a gene/organism name is known instead of an accession, providing clear when-to-use and when-not-to-use guidance.

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

A3.6/5.0
Disambiguation4/5

Most tools have highly specific purposes (e.g., crispr_grna_design vs base_editing_design vs prime_editing_design). However, there is some overlap in sequence analysis tools (characterize_sequence, sequence_report) and plasmid annotation tools (plasmid_annotate vs plasmid_deep_annotate) which could cause confusion.

Naming Consistency3/5

The naming pattern is largely consistent with snake_case verb_noun or noun_descriptor (e.g., primer_design, plasmid_annotate, fastq_trim). However, there are exceptions like 'batch', 'workflow', 'gc_content', and 'cloning_diagnose' which don't follow the verb_noun pattern consistently. Also, some names are phrases like 'golden_gate_from_parts'.

Tool Count2/5

With 101 tools, this server is extremely large and likely overwhelming for agents. Even for a comprehensive bioinformatics toolkit, this exceeds a manageable scope, risking agent confusion and inefficient tool selection. A more modular approach would be advisable.

Completeness4/5

The tool surface covers a wide range of bioinformatics workflows including sequence analysis, primer design, cloning, CRISPR, NGS, expression analysis, and data export. There are minor gaps such as lack of a dedicated protein structure prediction tool and limited off-target genome coverage, but overall the set is impressively complete for its domain.

Resources