# MCPMem

> Use this tool when you need to enable AI assistants to retain context and recall specific information across conversations. MCPMem solves the problem of ephemeral memory in AI interactions by providing persistent storage and semantic search capabilities. It accepts text-based inputs and outputs relevant memories, making it ideal for use cases requiring context retention and information retrieval.

Canonical page: https://skillsregistry.net/skills/designly1-mcpmem  
JSON: https://api.skillsregistry.net/v1/skills/designly1-mcpmem

## Description

Enables AI assistants to store and retrieve memories with semantic search capabilities using vector embeddings. Provides persistent memory storage with SQLite backend for context retention across conversations.

## Trust

- **Trust score (0–1):** 0.99
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-09-28

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/t5001amtth)
- **Repository:** <https://github.com/designly1/mcpmem>

## 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": "designly1-mcpmem"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/designly1-mcpmem` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/designly1-mcpmem/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