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Ligation / assembly reaction setup

ligation_setup
Read-onlyIdempotent

Work out how many microlitres of vector and insert to pipette to hit a target molar ratio, from each part's length and stock concentration. Handles one insert or several with independent equivalents (Gibson, Golden Gate, MoClo), reports pmol and ng per part alongside the volumes, and flags the two things that actually go wrong on a bench: a volume below what a pipette measures reliably, and a plan whose DNA does not leave room for buffer and enzyme. A molar ratio is about moles, so a shorter insert at 3 molar equivalents goes in at LESS mass than the vector — that conversion is the point.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
insertsNoMulti-fragment assembly: one entry per insert, each with its own length, stock and molar equivalents. Overrides insertLengthBp/insertNgPerUl/molarRatio when present.
vectorNgNoHow much backbone to put in the reaction, ng. This is the quantity you fix; every insert mass follows from the ratio.
molarRatioNoMolar equivalents of insert per vector. 3 is the usual starting point for a sticky-end ligation; 1-2 is common for Gibson and Golden Gate.
insertNgPerUlNoInsert stock concentration, ng/µL.
vectorNgPerUlYesBackbone stock concentration, ng/µL.
insertLengthBpNoInsert length in bp. Use `inserts` instead for a multi-fragment assembly.
vectorLengthBpYesBackbone length in bp.
vectorSequenceNoOptional backbone sequence — gives an exact molar mass from base composition instead of the 650 g/mol/bp average.
reactionVolumeUlNoTotal reaction volume, µL.

TDQS

A4.6/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true and idempotentHint=true, but the description adds valuable behavioral details beyond that: it 'flags the two things that actually go wrong on a bench' (pipette volume reliability and insufficient room for buffer/enzyme), and explains the conversion logic ('a shorter insert at 3 molar equivalents goes in at LESS mass than the vector'). This enriches understanding of what the tool returns and its design intent, without contradicting annotations.

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 a single dense paragraph that is front-loaded with purpose, then features, then a critical caveat. It is informative without being verbose, but it could be slightly more concise by trimming the last sentence's redundancy. However, every sentence earns its place by conveying essential context.

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 complexity (9 parameters, all schema-documented) and no output schema, the description adequately covers what the tool computes (volumes, pmol, ng), its multi-insert capability, and the important error flags. It tells the user what to expect without needing an output schema. The completeness is high for a calculation tool with clear annotations.

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 coverage is 100% for all 9 parameters, each with a description. The tool description adds conceptual meaning beyond the schema, such as how molar equivalents map to mass and the role of length and stock concentration. While it doesn't detail each parameter further, it provides the underlying logic that ties them together, which is valuable for an agent selecting parameters correctly.

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 purpose: 'Work out how many microlitres of vector and insert to pipette to hit a target molar ratio' – a specific verb and resource. It distinguishes itself from sibling DNA tools by emphasizing support for multi-fragment assemblies (Gibson, Golden Gate, MoClo) and its focus on pipetting volumes, which is not offered by generic calculators like dna_molarity or simulators like cloning_simulate.

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?

Provides clear context on when to use: 'Handles one insert or several with independent equivalents (Gibson, Golden Gate, MoClo)' and explains the molar ratio concept. It implies usage for assembly setup but does not explicitly name alternatives or exclusions. However, the context is sufficient for an agent to distinguish it from other tools in the suite.

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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