workbench

Functional enrichment (GO + Reactome)

functional_enrichment

Read-onlyIdempotent

Over-representation analysis: test which GO terms (biological process / molecular function / cellular component) and Reactome pathways are statistically enriched in a query gene list versus a background, using the hypergeometric test with Benjamini-Hochberg FDR correction across all tested terms. Uses bundled GO Consortium + Reactome reference data (human only). KEGG is not included (its license does not permit bundling gene sets).

Input Schema

TableJSON Schema

Name	Required	Description
`genes`	Yes	Query gene symbols (human, e.g. "TP53"). Case-insensitive. Capped at 5000.
`background`	No	Custom background/universe gene symbols. If omitted, defaults to every gene present in the bundled GO+Reactome dataset (the 'only annotated genes' convention, as used by g:Profiler) rather than the whole genome.
`collections`	No	Which term collections to test. Defaults to all four.
`maxTermSize`	No	Skip terms/pathways with more than this many background genes (matches clusterProfiler's default).
`minTermSize`	No	Skip terms/pathways with fewer than this many background genes.

TDQS

A4.6/5.0

Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint), the description discloses the statistical method, multiple testing correction (Benjamini-Hochberg), reliance on bundled reference data (human only), and the default background convention. No contradictions with annotations.

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 paragraph of three sentences, front-loading the core purpose and method. Every sentence adds essential information without redundancy.

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 no output schema, the description could specify the output format more explicitly, but it adequately covers the method, data sources, parameter defaults, and limitations. The 100% schema coverage compensates, making the overall description complete enough for its complexity.

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?

With 100% schema coverage, the description still adds value by explaining the default background meaning (only annotated genes, like g:Profiler) and the default maxTermSize (matching clusterProfiler). This contextualizes parameter behavior beyond their schema descriptions.

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 explicitly states it performs over-representation analysis for GO terms and Reactome pathways using the hypergeometric test with FDR correction. It clearly identifies the specific input (query gene list vs background) and output (enriched terms), distinguishing it from sibling tools like gene_expression or crispr_grna_design.

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 provides clear guidance on when to use this tool (for enrichment of GO and Reactome terms) and explicitly notes that KEGG is excluded due to licensing, which helps manage expectations. It does not list alternative tools for KEGG enrichment, but the context is sufficient for an agent to decide.

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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