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Metabolite volcano plot / 代谢组火山图

metabolite_volcano
Read-only

Metabolite volcano plot / 代谢组火山图 | USE WHEN: volcano plot of differential metabolites | ALIASES: 代谢火山图, volcano, 差异代谢物火山图 | RETURNS: 火山图

Input Schema

NameRequiredDescriptionDefault
data_jsonYes必填。数据对象(不是字符串)。矩阵形式:{"matrix": [[行1各列数值...], [行2...]], "rows": ["基因/特征名"], "cols": ["样本名"]};组学结果另可带 {"groups": ["组A","组A","组B"...]}(与 cols 一一对应)、{"p_values": [...], "log2fc": [...]}(若已有差异结果)。实测缺此字段会被拒:Field required: data_json

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

C2.5/5.0
Behavior2/5

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

Annotations already cover the safety profile (readOnlyHint=true, destructiveHint=false, openWorldHint=false), so the description only needed to add context. Instead it offers just "RETURNS: 火山图," which does not clarify whether the output is an image, a file path, base64 data, or a plot specification — with no output schema, that gap matters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is short and pipe-delimited, but the ALIASES segment (代谢火山图, volcano, 差异代谢物火山图) consumes space without helping an agent decide or invoke. The content is front-loaded, yet the brevity comes from under-specification rather than tight editing.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool that takes a nested, structurally strict data object and produces a plot, the description says nothing about expected data shape, statistical requirements, or the return value. The rich schema compensates for input details, but the output side is undocumented anywhere, leaving the definition incomplete.

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?

Only one parameter exists and the schema describes it at 100% coverage, including matrix shape, rows/cols mapping, optional groups, and p_values/log2fc, plus the exact validation error for a missing field. The description adds no meaning beyond that, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description does identify the operation as a volcano plot of differential metabolites, which distinguishes it from siblings such as metabolite_heatmap, metabolite_pca, and metabolite_plsda. However, most of the text restates the title and name, and the substantive scope (what input it accepts, what it computes) is left unstated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

"USE WHEN: volcano plot of differential metabolites" is a bare trigger phrase that largely repeats the tool name. There is no mention of when NOT to use it, no prerequisites (e.g. need p-values and log2fc), and no routing to siblings like metabolite_heatmap or metabolite_pathway for adjacent analyses.

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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