# EnrichMCP

> Use this tool when you need to create a semantic layer for AI agents from your data model, automating the generation of typed and discoverable tools with entity relationships. It solves problems of data discovery, schema complexity, and entity relationship mapping, providing a seamless interface for AI agents to interact with your data. By using EnrichMCP, you can streamline data integration and enable AI agents to make informed decisions with accurate and contextually relevant information.

Canonical page: https://skillsregistry.net/skills/featureform-enrichmcp  
JSON: https://api.skillsregistry.net/v1/skills/featureform-enrichmcp

## Description

Turns your data model into a semantic layer for AI agents, automatically generating typed, discoverable tools with entity relationships and schema discovery.

## Trust

- **Trust score (0–1):** 0.68
- **Verification tier:** scanned
- **Last scanned:** 2026-09-01

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-01

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/z3uo7i1kyd)
- **Repository:** <https://github.com/featureform/enrichmcp>

## 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": "featureform-enrichmcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/featureform-enrichmcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/featureform-enrichmcp/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