MCP GeneLab Server
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mcp-server-ladybugofficial
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TDQS
Scored across 24 tools
The find_* specialists are cleanly separated by data type (expression/methylation/abundance) and by scope (single vs common vs cross-type overlap), and `query` has explicit routing rules to prevent misuse. However, the schema/metadata cluster (get_node_metadata, get_neo4j_schema, get_relationship_metadata) and the plot-saving pair (fetch_plot, get_save_script) overlap enough that selection could be fuzzy. The lengthy descriptions largely resolve this, so only a couple of pairs are confusable.
Consistent snake_case throughout, and most names follow a verb_noun pattern (get_study_info, create_session, find_differentially_expressed_genes, set_output_directory, create_venn_diagram). Minor deviations exist: `query` is a bare verb with no object, and `visualize_schema`/`clean_mermaid_diagram` break the get/find/create cadence. Overall predictable with small inconsistencies.
At 24 tools the surface is heavy, sitting in the borderline range. Most clusters (7 find_* analyses, session/output management, schema metadata) are justified by the genuinely broad domain, but a few tools like create_chat_transcript, clean_mermaid_diagram, and visualize_schema feel auxiliary to the core KG-analysis purpose. The count is reasonable but slightly overweight.
Coverage is strong: single-assay and cross-assay analyses for all three differential data types, a DE/DM coupling tool, study/assay resolution, schema introspection, plotting, and a general Cypher fallback. The main gap is study discovery — there is no tool to search or list studies, so an agent must already know a study_id (e.g. OSD-267) or fall back to raw Cypher to find one.