# Feature Engineering MCP Server

> Feature Engineering MCP Server — ahutosh173-automated-feature-engineering-mcp. Use this tool when you need to automate feature engineering tasks, such as dataset profiling and feature planning, to streamline machine learning workflows. It solves problems related to manual feature engineering, providing efficient and scalable solutions through MCP-compatible hosts. The tool takes in dataset inputs and outputs optimized feature codes, making it ideal for data scientists and engineers working on complex data projects.

Canonical page: https://skillsregistry.net/skills/ahutosh173-automated-feature-engineering-mcp  
JSON: https://api.skillsregistry.net/v1/skills/ahutosh173-automated-feature-engineering-mcp

## Description

Exposes the automated feature engineering LangGraph agent as MCP tools, enabling dataset profiling, feature planning, and feature code execution through MCP-compatible hosts.

## Trust

- **Trust score (0–1):** 0.70
- **Verification tier:** verified
- **Last scanned:** 2026-09-04

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-09-04

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/r20pf9xzwc)
- **Repository:** <https://github.com/ahutosh173/automated-feature-engineering-mcp>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "ahutosh173-automated-feature-engineering-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/ahutosh173-automated-feature-engineering-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/ahutosh173-automated-feature-engineering-mcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt