# Loop MCP Server

> Use this tool when you need to process large datasets in batches or item-by-item, and require a server to manage and store the results of tasks performed by Large Language Models (LLMs). It solves problems of data overload and inefficient processing, providing an interface for inputting arrays and tasks, and outputting stored results with optional summarization. Ideal for use cases where LLMs need to handle multiple items or batches with specific tasks, and results need to be efficiently managed and retrieved.

Canonical page: https://skillsregistry.net/skills/smogili1-loop-mcp  
JSON: https://api.skillsregistry.net/v1/skills/smogili1-loop-mcp

## Description

Enables LLMs to process arrays item-by-item or in batches with a specific task, storing and retrieving results with optional summarization after completion.

## Trust

- **Trust score (0–1):** 0.70
- **Verification tier:** verified
- **Last scanned:** 2026-09-02

## Facts

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

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/bx572co3r2)
- **Repository:** <https://github.com/smogili1/loop_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": "smogili1-loop-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/smogili1-loop-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/smogili1-loop-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