shap-mcp

get_plot

Generate SHAP visualizations (summary, bar, waterfall, force, dependence, heatmap) to explain model predictions. Specify plot type and optional parameters to save the plot as a PNG and get its file path and URL.

Instructions

Generate a SHAP visualization, save it as a PNG file, and return file path and URL.

Parameters

plot_type : str One of 'summary', 'bar', 'waterfall', 'force', 'dependence', 'heatmap'. index : int | None Row index in dataset (required for waterfall and force). feature_name : str | None Feature name (required for dependence). top_n : int Max features to show (default: 10). color_feature : str | None Feature to color by for dependence plots. output_path : str | None Override default output directory path.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "plot_type": "<plot_type>"
    },
    "name": "get_plot"
  }
}

Input Schema

Name	Required	Default
`index`	No
`top_n`	No
`plot_type`	Yes
`output_path`	No
`feature_name`	No
`color_feature`	No	auto

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 22, 2026

TDQS

A3.6/5.0

Behavior3/5

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

No annotations are provided, so the description carries the burden. It discloses that the tool saves a PNG file and returns a file path and URL, which is useful. However, it doesn't mention side effects like file system writes, potential overwrites, or any permissions needed. It also doesn't describe the output schema beyond the return of path and URL, though an output schema exists.

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

Conciseness4/5

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

The description is well-structured with a clear summary sentence followed by a parameter list. It's front-loaded with the core action and output. The parameter list is concise and informative, though it could be slightly more compact by merging some lines.

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 has 6 parameters, an output schema, and no annotations, the description covers the essential usage details: plot types, conditional parameters, and output format. It doesn't mention error cases or edge conditions, but for a visualization tool, this is reasonably complete. The output schema likely covers return structure.

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 description coverage is 0%, so the description must compensate. It does well by explaining each parameter's purpose, including conditional requirements (index for waterfall/force, feature_name for dependence) and defaults (top_n=10, color_feature='auto'). This adds significant value beyond the raw schema.

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

Purpose4/5

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

The description clearly states the tool generates a SHAP visualization, saves it as PNG, and returns file path and URL. It lists the plot types, which helps distinguish it from siblings like get_feature_importance or explain_prediction. However, it doesn't explicitly differentiate from get_interaction, which might also produce plots.

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

Usage Guidelines3/5

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

The description implies usage by listing required parameters for certain plot types (e.g., index for waterfall/force, feature_name for dependence), but it doesn't explicitly state when to use this tool versus alternatives like get_feature_importance or explain_prediction. No exclusions or alternative routing is provided.

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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