# Loom

> Use this tool when you need to generate and explore text content interactively, leveraging AI model completions for creative writing, content development, and text refinement. Loom provides a collaborative text exploration environment, accepting text inputs and producing completed or expanded text outputs. Ideal for use cases requiring iterative text generation and refinement, such as content creation and writing workflows.

Canonical page: https://skillsregistry.net/skills/lyramakesmusic-loom  
JSON: https://api.skillsregistry.net/v1/skills/lyramakesmusic-loom

## Description

Loom is a collaborative text exploration MCP server built with Python using Starlette and Uvicorn. It provides base model completions for interactive text generation workflows, enabling users to explore and expand text content through AI model interactions. The implementation focuses on text-based exploration and completion tasks, making it useful for creative writing, content development, and iterative text refinement processes.

## Trust

- **Trust score (0–1):** 0.96
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

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

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/lyramakesmusic-loom)
- **Repository:** <https://github.com/lyramakesmusic/loom-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": "lyramakesmusic-loom"
    }
  }
}
```

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