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by com.seqbench

Expression heatmap clustering

expression_heatmap_cluster
Read-onlyIdempotent

Hierarchically cluster a genes x samples expression matrix (UPGMA/average, complete, or single linkage; Euclidean or correlation distance) and return the row/column leaf order, dendrogram merge trees, and row-z-scored values for the Clustered Expression Heatmap visualization.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
genesYesRow (gene) labels.
valuesYesgenes x samples numeric matrix — one row per gene, in the same order as `genes`.
linkageNoaverage = UPGMA (standard default), complete = farthest-neighbor, single = nearest-neighbor.average
samplesYesColumn (sample) labels.
zScoreRowsNoRow-wise z-score each gene's values before clustering and returning (the conventional 'relative expression' heatmap normalization — the dendrograms are computed on the same scaled matrix the heatmap shows, as in seaborn's clustermap(z_score=0) / pheatmap's scale="row").
clusterColsNoCluster (reorder) samples.
clusterRowsNoCluster (reorder) genes.
distanceMetricNocorrelation = 1 - Pearson r (the standard expression-heatmap default); euclidean = straight-line distance.correlation

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the agent knows it is a safe, deterministic operation. The description adds value by specifying the outputs (leaf order, merge trees, z-scored values) and describing the computation, which goes beyond the annotations. It does not mention potential limitations like matrix size or missing data handling.

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 one dense but efficient sentence that covers the core function, algorithmic options, and outputs without redundancy. It is front-loaded with the primary action and every clause contributes to understanding the tool.

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 the tool's complexity (8 params, no output schema), the description provides a solid overview of inputs, processing, and outputs. It names the return artifacts explicitly, which is helpful. It could be more explicit about the exact data structure of merge trees or edge-case behavior, but the core purpose and data flow are clear.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All 8 parameters have detailed descriptions in the schema (100% coverage), including the meaning of linkage, distanceMetric, and zScoreRows. The main description only briefly summarizes options already present in the schema, so it does not add substantial meaning beyond the schema.

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 hierarchically clusters a genes x samples expression matrix and returns specific artifacts (leaf order, dendrogram merge trees, row-z-scored values) for a heatmap visualization. This distinctly differentiates it from sibling tools like gene_expression or volcano_plot_data.

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 context is clear: it is a data-preparation step for the 'Clustered Expression Heatmap visualization.' However, it doesn't explicitly mention when to avoid using it or suggest alternative tools, which keeps it from a 5.

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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