# Mem0

> Use this tool when you need to efficiently manage and retrieve coding preferences, solutions, and documentation in a cloud-native environment. Mem0-MCP solves problems of disorganized code snippets and knowledge by providing a structured approach to storing and searching preferences through a server-based interface. It accepts inputs via SSE and outputs relevant code implementations, making it ideal for developers seeking to streamline their workflow.

Canonical page: https://skillsregistry.net/skills/mem0-coding-preferences  
JSON: https://api.skillsregistry.net/v1/skills/mem0-coding-preferences

## Description

Mem0-MCP provides a structured approach for managing coding preferences through an MCP server that integrates with mem0.ai. Built with Python using the FastMCP framework, it offers three main tools: adding coding preferences with comprehensive context, retrieving all stored preferences, and semantically searching through preferences to find relevant code implementations, solutions, and documentation. The server runs as a persistent process that agents can connect to via SSE, making it ideal for cloud-native environments where server and clients operate as decoupled processes. This implementation is particularly valuable for developers using Cursor who need efficient storage and retrieval of code snippets and programming knowledge.

## Trust

- **Trust score (0–1):** 0.65
- **Verification tier:** scanned
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** search
- **Updated:** 2026-09-02

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/mem0-coding-preferences)
- **Repository:** <https://github.com/mem0ai/mem0-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": "mem0-coding-preferences"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/mem0-coding-preferences` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/mem0-coding-preferences/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