# mcp-memory

> Use this tool when you need to enable MCP-based AI agents to retain information across sessions. It solves the problem of knowledge loss between interactions by providing persistent memory with semantic search capabilities. The tool takes in vector embeddings as input and outputs relevant information, making it ideal for use cases where contextual understanding and recall are crucial.

Canonical page: https://skillsregistry.net/skills/onekn8-mcp-memory  
JSON: https://api.skillsregistry.net/v1/skills/onekn8-mcp-memory

## Description

Provides persistent memory with semantic search for MCP-based AI agents, enabling them to store and recall information across sessions using vector embeddings.

## Trust

- **Trust score (0–1):** 0.70
- **Verification tier:** verified
- **Last scanned:** 2026-08-31

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-08-31

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/plc1ypg879)
- **Repository:** <https://github.com/oneKn8/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": "onekn8-mcp-memory"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/onekn8-mcp-memory` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/onekn8-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