# Memory-DB MCP Server

> Use this tool when you need to efficiently store and retrieve memories for AI agents, enabling semantic search and deletion of memories using custom embedding models. It solves problems of knowledge retention and retrieval, allowing agents to learn from experiences and adapt to new information. The Memory-DB MCP Server takes in memories and embedding models as inputs and outputs relevant memories based on semantic searches.

Canonical page: https://skillsregistry.net/skills/cunzai97-memory-db  
JSON: https://api.skillsregistry.net/v1/skills/cunzai97-memory-db

## Description

A lightweight vector-based memory system for AI agents with tools to store, semantically search, and delete memories, using your own embedding model.

## Trust

- **Trust score (0–1):** 0.67
- **Verification tier:** scanned
- **Last scanned:** 2026-08-30

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-08-30

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/ls6t3oxai5)
- **Repository:** <https://github.com/cunzai97/Memory-DB>

## 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": "cunzai97-memory-db"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/cunzai97-memory-db` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/cunzai97-memory-db/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