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workbench

by com.seqbench

DNA molarity calculator

dna_molarity
Read-onlyIdempotent

Nucleic-acid quantity conversions: molar mass, amount (pmol/nmol), molar and mass concentration, and copy number, from mass ± volume and either a length or a sequence.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoMolecule type.dsDNA
lengthNoLength in bp (dsDNA) or nt (ssDNA/ssRNA). Ignored when a sequence is given.
massNgNoMass in nanograms.
sequenceNoOptional sequence — overrides length and gives an exact molar mass from base composition.
volumeUlNoVolume in microlitres (0 = unknown; needed for concentration).
targetUnitNoUnit for targetConcentration.nM
targetVolumeUlNoFinal volume of the diluted sample, in µL.
targetConcentrationNoOptional: the concentration you need. Supplying it (with volumeUl, so there is a stock concentration to dilute FROM) returns a `dilution` plan — µL of stock and µL of diluent — instead of leaving C1V1 = C2V2 to be done by hand. The stock and the target may be quoted in different units; the molar mass converts between them.

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare the tool read-only and idempotent, so the description only needs to add behavioral context. It adds the input relationship ('from mass ± volume and either a length or a sequence') and the list of conversions, but it does not describe return shape or edge cases. 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 a single, tightly structured sentence that front-loads the domain and lists the outputs and inputs without filler. Every clause contributes meaningful information.

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?

For a read-only calculator with a fully documented schema, the description provides enough context to understand what the tool does and what inputs it needs. The lack of an output schema means a brief note about the return format would be useful, but it is not critical given the tool's simplicity.

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?

Input schema coverage is 100%, with all eight parameters documented including defaults and enums, so the description does not need to carry parameter meaning. The description adds only a high-level summary of the key inputs, which is helpful but not necessary for parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as a nucleic-acid quantity converter and enumerates the concrete quantities it produces (molar mass, amount, molar/mass concentration, copy number) with the necessary inputs. It does not explicitly distinguish itself from sibling tools, but its domain is distinct enough that an agent can identify its purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when the tool should be used: whenever nucleic-acid molarity, concentration, or copy-number conversions are needed. It does not explicitly name alternatives or state when not to use the tool, so some inference is required.

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