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by com.seqbench

Design a ribosome binding site (OSTIR)

rbs_design
Read-onlyIdempotent

Design a 5' UTR / ribosome binding site for a given CDS. Generates a spread of Shine-Dalgarno cores and SD-to-start spacings, scores every one with OSTIR in the context of your own CDS (which matters — the rate depends on how the RBS interacts with that CDS's 5' folding), and returns them ranked. Supply targetExpression to rank by closeness to a target rate instead of by maximum strength, and supply your existing 5' UTR to get a measured baseline and fold-change for each candidate. Runs ViennaRNA on a shared service and is therefore rate limited (see 429/503). PREDICTED, NOT MEASURED (Spearman ρ = 0.39 on two 5' UTR datasets it was not fitted to). Spearman ρ = 0.39 against measured expression on two 5' UTR datasets it was not fitted to (Gilliot & Gorochowski, Nucleic Acids Res 2024;52(13):e58). The widely quoted 53% within 2-fold / 91% within 10-fold are calibration residuals on the fitting set, not held-out validation. Valid for: translation INITIATION only, in E. coli-like Gram-negative hosts (the model is parameterised on the E. coli anti-Shine-Dalgarno sequence). Rankings within one construct context; the absolute value has no units and no meaning.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cdsYesCoding sequence, raw or FASTA, starting at its start codon. Only the 5' end affects the prediction, so the first ~90 nt is enough. A, C, G, T/U only. Max 3,000 nt.
limitNoHow many ranked candidates to return. 1-60.
leaderNoOptional 5' context upstream of the designed RBS — the transcribed leader from your promoter. Affects the standby-site term. Defaults to a 20 nt unstructured poly-A leader.
currentUtrNoOptional: your existing 5' UTR (everything upstream of the start codon). Scored as a baseline so each candidate gets a fold-change against it.
targetExpressionNoOptional target rate on OSTIR's arbitrary scale. Candidates are then ranked by closeness to it (log-ratio) rather than by maximum strength. Only meaningful against a number produced by this same tool.
antiShineDalgarnoNoOptional anti-Shine-Dalgarno sequence (the 16S rRNA 3' end) to model a non-E. coli host. Omit to use OSTIR's own E. coli default.

TDQS

A4.6/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint and idempotentHint), the description discloses critical behavioral traits: the tool is PREDICTED not measured, explains the validation caveats (53%/91% are calibration residuals), and notes rate limiting on the shared service. This adds substantial context that annotations alone cannot provide.

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 detailed yet front-loaded with the core purpose. Some repetition of the validation data appears twice, which could be streamlined, but overall it is efficient and informative.

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?

Given the tool has 6 parameters (only 1 required), no output schema, and moderate complexity, the description is complete. It explains the effect of each optional parameter (targetExpression, currentUtr, antiShineDalgarno), the return behavior (ranked candidates, baseline fold-change), and limitations (E. coli-like hosts, no absolute value meaning).

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 3 is appropriate. The description does not add significant semantic nuance beyond the schema; it mentions the leader and currentUtr parameters in context but doesn't elaborate on types or constraints.

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 designs a 5' UTR / ribosome binding site for a given CDS, using OSTIR scoring. It distinguishes itself from siblings like rbs_predict (which predicts for a given RBS) and other design tools by naming the specific method and output structure.

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 provides explicit guidance on when to use this tool: for translation initiation design in E. coli-like hosts, with notes on the predictive nature (Spearman ρ=0.39, not fitted to held-out data) and that it is for ranking within one construct, not absolute values. It also warns about rate limits (429/503) from shared ViennaRNA service.

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.

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