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by com.seqbench

RNA Secondary Structure (MFE)

rna_fold
Read-onlyIdempotent

Predict an RNA secondary structure by minimum free energy (MFE) using a Zuker dynamic program with Turner 1999 nearest-neighbor stacking energies (no pseudoknots). Returns the dot-bracket structure, the estimated MFE (kcal/mol), and the list of base pairs. A from-scratch, in-browser implementation (there is no usable browser ViennaRNA); the simplified loop energy model makes the MFE a good comparative estimate, not a lab-grade absolute. PREDICTED, NOT MEASURED. No held-out skill statistic is claimed for this implementation. The loop model omits terminal mismatches, dangling ends, coaxial stacking and special hairpins that ViennaRNA and RNAstructure include, so the MFE is a comparative estimate between candidates rather than a lab-grade absolute. Valid for: a single strand up to 600 nt at a fixed 37 °C. No pseudoknots, no two-strand hybridisation, and no temperature dependence — a structure predicted here is not a structure at your annealing temperature.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sequenceYesNucleotide sequence (raw or FASTA; IUPAC accepted).

TDQS

A4.3/5.0
Behavior5/5

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

Annotations already provide readOnlyHint and idempotentHint, but the description adds significant behavioral context: it is an in-browser implementation with a simplified loop model, the MFE is a comparative estimate not lab-grade, and it explicitly states 'PREDICTED, NOT MEASURED'. This goes well beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

The description is front-loaded with the core function and outputs, but it is verbose and contains some redundancy (e.g., the MFE comparative estimate is mentioned twice). While informative, it could be more concise without losing essential caveats.

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 only one parameter and no output schema, the description fully explains the input requirements, output contents, and critical limitations. It covers valid ranges, conditions, and accuracy caveats, making it complete for an agent to use 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 schema already provides 100% coverage for the single parameter 'sequence' (description: 'Nucleotide sequence (raw or FASTA; IUPAC accepted).'). The tool description adds no additional parameter-specific details beyond restating the general purpose, so the baseline score of 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 clearly states the tool predicts RNA secondary structure by minimum free energy using the Zuker algorithm with Turner 1999 energies. It specifies the output types (dot-bracket, MFE, base pairs) and distinguishes it from more complex tools like ViennaRNA/RNAstructure, making the purpose unambiguous.

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 description explicitly lists valid inputs (single strand up to 600 nt, 37°C) and limitations (no pseudoknots, no two-strand hybridization, no temperature dependence), which helps the agent decide when to use this tool. However, it does not mention alternative tools from the sibling list (e.g., oligo_cofold for two strands) for comparison.

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