# Relational DB Seeder MCP Server

> Relational DB Seeder MCP Server — namant98-db-seeder. Use this tool when you need to populate relational databases with custom data, resolving foreign key relationships automatically, to support AI model training or testing with realistic data. It enables LLMs to inspect database schemas and seed databases efficiently, solving data preparation challenges for various use cases. Ideal for applications requiring synthetic or anonymized data, such as machine learning model development or database testing.

Canonical page: https://skillsregistry.net/skills/namant98-db-seeder  
JSON: https://api.skillsregistry.net/v1/skills/namant98-db-seeder

## Description

Enables LLMs to inspect database schemas and seed relational databases with custom data while automatically resolving foreign key relationships.

## Trust

- **Trust score (0–1):** 0.69
- **Verification tier:** scanned
- **Last scanned:** 2026-09-03

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-09-03

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/b2egdtw0ck)
- **Repository:** <https://github.com/NamanT98/DB-Seeder>

## 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": "namant98-db-seeder"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/namant98-db-seeder` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/namant98-db-seeder/pull`

---
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