OpenXAI MCP Server

MIT License

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generate_explanation

Create interpretable explanations for model predictions using methods like LIME, SHAP, and integrated gradients. Input data and model details to clarify decision processes for AI systems.

Instructions

Generate explanations for model predictions using OpenXAI explainers

Input Schema

Name	Required	Description
`data_sample`	Yes	JSON string of the input data sample to explain
`method`	Yes	Explanation method to use (lime, shap, integrated_gradients, etc.)
`model_info`	Yes	Information about the model being explained

Input Schema (JSON Schema)

{
  "properties": {
    "data_sample": {
      "description": "JSON string of the input data sample to explain",
      "type": "string"
    },
    "method": {
      "description": "Explanation method to use (lime, shap, integrated_gradients, etc.)",
      "enum": [
        "lime",
        "shap",
        "integrated_gradients",
        "gradcam",
        "guided_backprop"
      ],
      "type": "string"
    },
    "model_info": {
      "description": "Information about the model being explained",
      "properties": {
        "data_name": {
          "type": "string"
        },
        "ml_model": {
          "type": "string"
        }
      },
      "type": "object"
    }
  },
  "required": [
    "method",
    "data_sample",
    "model_info"
  ],
  "type": "object"
}

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