# MCP Memory

> Use this tool when you need to personalize user experiences by storing and retrieving their preferences and behaviors across conversations. It solves the problem of inconsistent user interactions by enabling MCP clients to remember and adapt to individual user needs. With vector search technology, it takes in user data as input and outputs relevant preferences and behaviors to inform subsequent conversations.

Canonical page: https://skillsregistry.net/skills/doko89-mcp-memory  
JSON: https://api.skillsregistry.net/v1/skills/doko89-mcp-memory

## Description

Enables MCP clients to remember user preferences and behaviors across conversations using vector search technology.

## Trust

- **Trust score (0–1):** 0.93
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/ver2mnfsb7)
- **Repository:** <https://github.com/doko89/mcp-memory>

## 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": "doko89-mcp-memory"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/doko89-mcp-memory` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/doko89-mcp-memory/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