workbench

Diagnostic digest planner

diagnostic_digest

Read-onlyIdempotent

Pick the restriction digest that tells your intended construct apart from the wrong ones on a screening gel. Digests every candidate, works out which bands would actually resolve at the chosen agarose percentage (size ratio, the gel's resolving window, and whether a band is too faint to score), and ranks single enzymes — then buffer-checked pairs if no single one works. The criterion is separating the INTENDED construct from every alternative; telling the alternatives apart from each other is reported as a bonus. Get the alternatives from assembly_outcomes.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`enzymes`	No	Restrict the search to these enzymes (e.g. the ones on your bench). Default is the whole curated set of 49. An unrecognised name is rejected rather than skipped, so "does not discriminate" always means it was actually tried.
`circular`	No	Treat every candidate as a circular plasmid.
`intended`	Yes	The construct you designed, raw or FASTA.
`alternatives`	Yes	The wrong constructs to screen against, 1-12. assembly_outcomes emits these ready-made as `forDiagnosticDigest`.
`includePairs`	No	Whether to search double digests. "auto" adds them only when no single enzyme separates everything.	auto
`agarosePercent`	No	Agarose percentage of the screening gel. Snapped to the nearest tabulated value (0.5, 0.7, 1, 1.2, 1.5, 2) and the one used is reported back, since the resolving window is what decides which bands separate.
`maxSuggestions`	No	How many ranked options to return.

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, but the description adds substantial behavioral detail: how band resolution is calculated (size ratio, resolving window, faintness), the auto-enabling of buffer-checked pairs, rejection of unrecognized enzyme names, and the reporting of the actual agarose percentage used. These are valuable beyond the annotations and do not contradict them.

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, well-structured paragraph that starts with the core purpose and flows through the process and key nuances. Every sentence adds unique information, with no filler or repetition. It is front-loaded with the primary goal and then provides supporting details.

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?

For a tool with 7 parameters and no output schema, the description covers all key aspects: what it does, how it works, how to obtain inputs, the ranking criteria, optional parameters, and the meaning of edge cases. It is sufficiently complete for an agent to use it correctly without additional documentation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 100% schema description coverage, the description substantially enhances parameter understanding: it explains alternatives (wrong constructs from assembly_outcomes), the auto/always/never nuance of includePairs, the snapping behavior of agarosePercent and its effect on resolution, and the rejection of unrecognized enzymes. This adds meaning beyond the schema definitions.

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 uses a specific verb (pick) and resource (restriction digest) and clearly explains the purpose: distinguishing an intended construct from alternatives on a screening gel. It goes beyond a generic statement by detailing the digestion, band resolution, ranking, and the criterion (separating intended from every alternative), which differentiates it from sibling tools like restriction_sites or virtual_gel.

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 clearly implies when to use it: when you need to select a diagnostic digest for construct screening. It mentions gathering alternatives from assembly_outcomes, which is an explicit usage hint. However, it does not explicitly state when not to use it or explicitly name alternative tools, but the context is clear enough for an agent.

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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