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Golden Gate from part plasmids

golden_gate_from_parts
Read-onlyIdempotent

Golden Gate as the reaction runs: digest pre-domesticated part plasmids with a Type IIS enzyme and assemble them in the order their OVERHANGS dictate. The fragment released from each part is the one carrying no recognition site (the site goes out with the backbone, which is why a mis-ordered assembly is not re-cut), and the assembly order is an OUTPUT — a set whose overhangs do not close into a single cycle has no product, and the reason is the answer. Distinct from cloning_simulate's goldengate method, which does the other job: designing the primers that ADD the sites to BARE parts, assembled in the order you list them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
partsYesThe part plasmids: [{ name, sequence, circular }]. Each needs two Type IIS sites facing INWARDS at the part. `circular` defaults to true, which is what a level-0 part plasmid is. 2-24 parts, each at most 30,000 bp and 200,000 bp in total.
enzymeNoType IIS enzyme (BsaI, BsmBI, BbsI, SapI). NEB's variant names (BsaI-HFv2, BbsI-HF, BsmBI) fold to the parent enzyme; any other name is rejected rather than substituted, since site, spacer and overhang length are all enzyme-specific.BsaI

TDQS

A4.3/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true and idempotentHint=true, which already cover safety and idempotency. The description adds meaningful behavioral context beyond annotations: it explains that the fragment released carries no recognition site, that the assembly order is an output, and that a set of overhangs that doesn't close into a cycle produces no product. This is valuable disclosure of the tool's internal logic without contradicting any annotation.

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 long but well-structured: it front-loads the core action, then explains the mechanism, output behavior, and finally the distinction from a sibling. Each sentence carries necessary information for a complex reaction. It could be slightly tightened, but the structure is logical and the density is justified by the technical depth.

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?

Given the tool's complexity and the absence of an output schema, the description adequately covers the key expectations: what the tool does, why the sites are needed, how the order is determined, and what happens when overhangs don't close. It does not describe the exact return format, but the description implies the assembly order is the output. For an agent calling this tool, the essential context is present.

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 description coverage is 100%, so the baseline is 3. The description adds only conceptual context about overhangs dictating order and enzyme-specificity, but these points are largely implicit in the schema (e.g., 'Each needs two Type IIS sites facing INWARDS'). It does not add new syntactic or semantic details beyond what the schema already provides, so a 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 states a specific verb and resource: 'digest pre-domesticated part plasmids with a Type IIS enzyme and assemble them in the order their OVERHANGS dictate.' It clearly distinguishes the tool from cloning_simulate's goldengate method, which is named explicitly. The purpose is unambiguous and sets it apart from siblings without needing to inspect schemas.

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 contrasts this tool with cloning_simulate's goldengate method, stating that the other tool 'designs the primers that ADD the sites to BARE parts, assembled in the order you list them.' This gives clear when-to-use and when-not-to-use guidance. It also explains the consequence of a mis-ordered assembly, which informs the user on what to expect.

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