# mcp-shared-memory

> Use this tool when you need to manage shared memory resources across multiple processes or applications, solving issues of data consistency and synchronization. It provides a server-based solution, accepting input from various sources and outputting unified memory access, facilitating collaboration and data exchange. Ideal for use cases requiring concurrent data access, such as distributed computing or real-time data processing.

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

## Description

mcp-shared-memory server

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** other
- **Updated:** 2026-05-06

## Source

- **Source listing:** [GitHub](https://github.com/tlemmons/mcp-shared-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": "tlemmons-mcp-shared-memory"
    }
  }
}
```

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