workbench

Base Editing Designer

base_editing_design

Read-onlyIdempotent

Design cytosine (CBE, C→T) or adenine (ABE, A→G) base-editing gRNAs for an SpCas9 target: for each NGG gRNA it reports every editable base inside the editor's activity window, flags bystander edits (more than one editable base in the window), and — with a CDS reading frame — classifies each edit's amino-acid consequence (silent / missense / nonsense / stop-loss). Bystander-free guides are ranked first. Handles both strands (a C→T on the protospacer of a reverse-strand guide is reported as the forward-strand G→A).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`editor`	No	Base editor: be3/be4max (CBE, C→T) or abe7.10/abe8e (ABE, A→G).	be4max
`target`	Yes	Nucleotide sequence (raw or FASTA; IUPAC accepted).
`frameStart`	No	Optional 1-based CDS reading-frame start, to classify each edit's amino-acid consequence.
`targetPosition`	No	Optional 1-based forward-strand position of the base you intend to edit; only guides whose window covers it are returned.

TDQS

A4.5/5.0

Behavior4/5

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

The description adds behavioral details beyond annotations (readOnlyHint=true): it explains strand handling, reporting of forward-strand changes for reverse guides, and classification of edits. Annotations already indicate safety, so the additional context is valuable. It lacks details on output format or pagination but is sufficient.

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 paragraph that is front-loaded with the main purpose and contains no fluff. It is slightly long but every sentence adds value. It could be slightly more structured (e.g., bullet points) but remains clear and efficient.

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 no output schema, the description fully explains what the tool returns: reported editable bases, bystander flags, amino-acid consequences, and ranking of bystander-free guides. It covers strands and editing types completely. No gaps are present.

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?

With 100% schema coverage, the description still adds significant meaning: explaining what edible bases are, what bystander edits mean, and how frameStart affects amino-acid classification. This goes beyond the schema descriptions, providing critical context for proper parameter usage.

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 designs cytosine (CBE, C→T) or adenine (ABE, A→G) base-editing gRNAs for SpCas9 targets, reports editable bases, flags bystander edits, and classifies amino-acid consequences. It distinguishes itself from sibling tools like crispr_grna_design and prime_editing_design by specifying the base editing context.

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 implies usage for base editing design and differentiates from siblings by naming specific editors (CBE, ABE). However, it does not explicitly state when not to use this tool or provide alternatives, leaving some inference to the 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.

Resources

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