shap-mcp

get_feature_importance

Retrieve the top features from stored SHAP values, ranked by impact. Use top_n to set the number of features to return.

Instructions

Return global feature importance from stored SHAP values.

Parameters

top_n : int Number of top features to return (default: 10).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_feature_importance"
  }
}

Input Schema

Name	Required	Description	Default
`top_n`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 22, 2026

TDQS

A4/5.0

Behavior3/5

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

With no annotations, the description carries the behavioral burden. It discloses that the tool reads 'stored SHAP values' and returns global importance, implying a read-only operation. It does not mention ordering, error behavior, or what happens if SHAP values are absent, but 'top_n' implies a ranked result.

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 compact and front-loaded with the core purpose. The parameter documentation is minimal and directly useful, with no filler or redundant restatement of the tool name.

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?

For a simple read-only tool with one optional parameter and an output schema, the description is largely complete. It covers purpose, source, and parameter semantics. It could add explicit usage guidance relative to siblings, but that gap is minor given the clear purpose and available output schema.

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?

The schema provides only the default value for top_n, while the description explains its meaning: 'Number of top features to return'. This adds real semantic value beyond the schema, fully compensating for the 0% schema description coverage.

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 states a specific verb ('Return') and a specific resource ('global feature importance from stored SHAP values'). The word 'global' distinguishes it from sibling tools like explain_prediction (local) and get_interaction, and the source ('stored SHAP values') clarifies it is a read of precomputed results.

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 when to use the tool: when global feature importance from SHAP values is needed. However, it does not explicitly state when not to use it or name alternatives such as explain_prediction or get_interaction, leaving the routing decision to inference.

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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