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musharna

plant-genomics-mcp

by musharna

GO / KEGG Enrichment (gene list)

go_enrichment
Read-onlyIdempotent

Tests a gene list for over-representation in GO terms and KEGG pathways using g:Profiler, revealing the biological themes enriched in the set.

Instructions

GO + KEGG over-representation analysis for a gene LIST via g:Profiler g:GOSt (biit.cs.ut.ee/gprofiler; free, no API key). Unlike locus_go_annotations (one locus → its terms), this answers 'what is my gene SET enriched for?' — the dominant question for a differential-expression or co-expression cluster. loci is the query gene list (e.g. AT-codes for Arabidopsis, RAP-DB IDs for rice). sources defaults to GO:BP/GO:MF/GO:CC + KEGG; user_threshold is the g:SCS-corrected significance cutoff (default 0.05). Optional background sets a custom statistical domain (default: all annotated genes). Returns enriched[] (term_id/name/p_value/intersection_size/…, capped at top_n by p-value) plus unmapped[] — query loci g:Profiler could not recognize, surfaced so a locus-namespace mismatch is visible. Defaults to arabidopsis_thaliana; pass organism= for any of the 12 species.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lociYesQuery gene set, e.g. ['AT2G46830', 'AT1G01060', ...]
top_nNoMax terms returned, sorted by p-value (1–200, default 50)
sourcesNoAnnotation sources to test (default: all four)
organismNoPlant organism — accepts canonical slug (arabidopsis_thaliana), scientific or common name, or NCBI taxidarabidopsis_thaliana
backgroundNoOptional custom statistical background gene set
user_thresholdNoSignificance cutoff, g:SCS-corrected (default 0.05)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
mappedYesLoci g:Profiler recognized
sourcesYesAnnotation sources queried
enrichedYes
organismYesCanonical organism slug
returnedYesTerms in enriched[] after the top_n cap
unmappedYesLoci g:Profiler could not map
query_sizeYesNumber of loci submitted
total_termsYesSignificant terms before the top_n cap
gprofiler_idYesg:Profiler organism ID used, e.g. athaliana
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description adds significant behavioral context: it names the upstream service (g:Profiler), notes it's free and requires no API key, explains the return of unmapped loci for detecting namespace mismatches, and describes the significance correction (g:SCS). 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 with each sentence contributing essential information. It starts with the core purpose, then immediately differentiates from sibling, explains key parameters, and notes return values. No filler; every sentence earns its place.

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 the tool's complexity (6 parameters, 1 required, output schema present, extensive annotations), the description covers all essential aspects: purpose, usage context, parameter behavior, service details, return format, and organism defaults. It is fully sufficient for an AI agent to select and invoke the tool correctly.

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 coverage is 100% with descriptions per parameter. The description adds value by explaining the role of 'loci' with examples, summarizing default behavior for sources and thresholds, and clarifying the purpose of optional parameters like background. This goes beyond the schema's structural 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 clearly states it performs GO/KEGG over-representation analysis for a gene list, and explicitly contrasts with the sibling tool 'locus_go_annotations' which handles single loci. The verb 'answers what is my gene SET enriched for?' is specific and distinguishes the tool's purpose.

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 by contrasting with locus_go_annotations and contextualizing the tool for differential-expression or co-expression cluster analysis. It could be improved with explicit when-not-to-use scenarios, but the sibling differentiation is effective.

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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