# rlm-mcp

> rlm-mcp — nexttokens-rlm-mcp. Use this tool when you need to optimize code context windows and scan large files efficiently. The rlm-mcp tool solves problems related to processing extensive codebases by effectively managing and scanning large files. It takes in code files as input and outputs optimized context windows, ideal for use cases involving large-scale code analysis, particularly with git repositories.

Canonical page: https://skillsregistry.net/skills/nexttokens-rlm-mcp  
JSON: https://api.skillsregistry.net/v1/skills/nexttokens-rlm-mcp

## Description

MCP to optimize Claude code context window and effectively scan large files and code

## Trust

- **Trust score (0–1):** 0.94
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** MIT
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/NextTokens/rlm-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": "nexttokens-rlm-mcp"
    }
  }
}
```

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