# Memory MCP

> Use this tool when you need to persist and manage conversation history in large language models, solving problems of context loss and inconsistent responses. It allows input of conversation data and outputs stored memories, utilizing a MongoDB database for persistence. Ideal for use cases requiring contextual understanding and continuity across multiple interactions.

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

## Description

A Model Context Protocol server that allows saving, retrieving, adding, and clearing memories from LLM conversations with MongoDB persistence.

## Trust

- **Trust score (0–1):** 0.98
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-09-19

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/c7pukx83uq)
- **Repository:** <https://github.com/JamesANZ/memory-mcp>

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

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