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workbench

by com.seqbench

Run a tool using session entries

session_run

Run any SeqBench tool, resolving selected arguments from a session's named entries instead of pasting them inline, and optionally store selected result fields back into the session by name. This is the main way to chain a multi-part design (vector + insert + primers) across calls without shuttling raw sequences through your own context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
argsNoAdditional literal arguments, merged with the ones resolved from the session.
toolYesName of any non-meta SeqBench tool (not batch, workflow, or another session_* tool).
sessionIdYes
writeBackNoMap of { resultFieldName: sessionEntryName } — stores selected fields of the result back into the session under these names.
fromSessionNoMap of { toolArgName: sessionEntryName } — resolves each named tool argument from the session before running.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations indicate mutation (readOnlyHint=false) and no idempotency. Description adds details on how arguments are resolved from session and optional write-back of results, which clarifies behavior beyond annotations. No contradiction.

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?

Two-sentence description that is concise and front-loaded: first sentence explains mechanism, second gives typical use case. No unnecessary words, efficient for agent understanding.

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 complexity of a meta-tool with 5 parameters and no output schema, description provides a solid overview of purpose and mechanism. Could mention error handling or required permissions, but adequately covers the main workflow.

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 provides 80% parameter descriptions. Description adds context by explaining the chaining use case and how fromSession/writeBack work, giving meaning beyond raw schema. For sessionId, schema lacks description but description implies its role in session identification.

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?

Clearly states verb 'Run', resource 'SeqBench tool', and explains core feature of resolving arguments from session entries. Differentiates from other tools by describing it as the main chaining method and specifying it applies to non-meta tools.

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?

Explicitly states when to use: for chaining multi-part designs without shuttling raw sequences. Implies alternatives (inline pasting) but does not explicitly list when not to use or alternative tools. Good context but could be more precise.

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