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shap-mcp

shap-mcp

by Manas-maker
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

load_model

Load a model file from a local path, URL, or upload and instantiate the appropriate SHAP explainer for model interpretability. Specify model type; deep models require a background dataset.

Instructions

Load a model file and instantiate the appropriate SHAP explainer.

Note: Prompt the user to provide local filesystem paths on their machine, public URLs, or upload via http://localhost:8765/ui/. Files attached directly in chat are stored in a cloud container (/mnt/user-data/) that local tools cannot reach.

Parameters

model_path : str | None Path to a local .joblib or .pkl model file. model_url : str | None Public URL to download a model file. model_type : str One of 'tree', 'linear', 'deep', 'kernel'. Default is 'tree'. background_path : str | None Path to background CSV dataset (optional for tree/kernel, required for deep).

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": "load_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_urlNo
model_pathNo
model_typeNotree
background_pathNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description must carry the weight. It discloses the operational constraint about cloud-container paths, which is a non-obvious behavioral detail. It also implies the tool is a prerequisite for other tools. It doesn't state whether it mutates state or returns the explainer, but the output schema exists, and the description is focused on prerequisites, which is beyond what schema conveys.

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-organized: a purpose sentence, a critical usage note, then parameter definitions. The warning about cloud paths is space-efficient and high-value. Slightly repetitive in noting upload methods, but overall concise and front-loaded with the most important behavioral caveat.

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 4-parameter tool with zero schema descriptions and no annotations, the description covers the key decision points: which parameter to set, required vs optional, and the path accessibility warning. It doesn't describe return values, but an output schema exists, so that's not required. The description is sufficient for correct invocation.

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%, but the description explains each parameter's purpose and optionality (model_path vs model_url as alternatives, background_path required for deep, optional for tree/kernel). It adds meaningful semantics beyond raw schema types, which is essential given zero 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 clearly states the tool loads a model file and instantiates a SHAP explainer, specifying the model types (tree, linear, deep, kernel) and the input sources (local path, URL, or upload). This distinguishes it from sibling tools that focus on analysis/explanation, not loading.

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?

It provides explicit guidance on when to use this tool (before running analyses) and, crucially, what not to do: it warns that files attached in chat are stored in a cloud container that local tools cannot reach, so the user must provide accessible paths. It doesn't explicitly mention alternatives (sibling tools are analysis tools, not load alternatives), but the context is clear.

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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