# memory-slim

> Use this tool when you need to optimize memory usage for AI assistants, as it reduces context window tokens by 54.9% while preserving full functionality, making it ideal for applications with limited memory resources. It takes in AI assistant workloads and outputs optimized memory usage, allowing for more efficient processing. This tool is particularly useful in resource-constrained environments where memory optimization is crucial.

Canonical page: https://skillsregistry.net/skills/mcpslim-memory-slim  
JSON: https://api.skillsregistry.net/v1/skills/mcpslim-memory-slim

## Description

Memory MCP server optimized for AI assistants, reducing context window tokens by 54.9% while preserving full functionality.

## Trust

- **Trust score (0–1):** 0.68
- **Verification tier:** scanned
- **Last scanned:** 2026-09-01

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-09-01

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/ybhgijvwv1)
- **Repository:** <https://github.com/mcpslim/memory-slim>

## 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": "mcpslim-memory-slim"
    }
  }
}
```

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