# Mnemexa MCP

> Use this tool when you need to enable AI agents to retain memory of user preferences and context across multiple interactions. The Mnemexa MCP provides persistent, self-optimizing memory, solving problems of knowledge loss and inconsistency between sessions. It accepts input from AI agents and outputs shared knowledge, ideal for use in multi-agent systems and conversational AI applications.

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

## Description

Provides persistent, self-optimizing memory for AI agents, enabling them to remember preferences and context across sessions and share knowledge across multiple agents.

## Trust

- **Trust score (0–1):** 0.86
- **Verification tier:** scanned
- **Last scanned:** 2026-09-19

## Facts

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

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/uix0grw1sm)
- **Repository:** <https://github.com/mnemexa/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": "mnemexa-mcp"
    }
  }
}
```

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