SLM Agent by ScaleDown

An MCP server + Claude Code plugin that scans your codebase for AI API calls and shows you exactly where and how to integrate ScaleDown SLMs to cut costs by up to 95%.

Privacy: This plugin analyzes your code locally. No source code or codebase data is ever sent to ScaleDown servers. The only network calls are to ScaleDown's API endpoints at runtime, when your application calls them with the SCALEDOWN_API_KEY you provide.

What it does

1. Evaluates — scans your project for OpenAI, Anthropic, LangChain, LlamaIndex, Cohere, and other AI API calls
2. Plans — generates a structured migration plan showing which ScaleDown SLM fits each call site:
   • sd_compress — reduce context tokens 50–70% before calling your LLM (for RAG, long docs)
   • sd_classify — replace frontier LLM classification calls (~95% cheaper)
   • sd_extract — replace frontier LLM entity extraction calls (~95% cheaper)
   • sd_summarize — replace frontier LLM summarization calls (~90% cheaper)
3. Migrates — applies the changes to your code with your approval, adapting to your real variable names

Installation

Claude Code (recommended)

/plugin install scaledown-team/SLM_Agent

Then in any project:

/slm-agent:evaluate

The plugin automatically configures the MCP server and registers the /slm-agent:evaluate skill. No further setup needed.

Cursor

Add to your project's .cursor/mcp.json (or ~/.cursor/mcp.json for global):

{
  "mcpServers": {
    "scaledown-slm-agent": {
      "command": "npx",
      "args": ["-y", "@scaledown/migration-agent"]
    }
  }
}

Then ask Cursor: "Use the scaledown-slm-agent tools to evaluate this codebase for ScaleDown integration opportunities."

VS Code (Copilot / any MCP-compatible extension)

Add to your workspace .vscode/mcp.json:

{
  "servers": {
    "scaledown-slm-agent": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@scaledown/migration-agent"]
    }
  }
}

Manual (any MCP client)

npm install -g @scaledown/migration-agent

Then configure your MCP client to run: scaledown-agent

MCP Tools

The server exposes four tools that any MCP-compatible AI assistant can call:

| Tool | Description | |---|---| | get_ai_detection_patterns | Returns grep-compatible regex patterns for all supported AI providers | | get_integration_template | Returns before/after code showing exactly how to integrate ScaleDown | | generate_migration_plan | Generates a full structured markdown migration plan from a list of findings | | save_migration_report | Writes the migration plan markdown to scaledown-report.md in the user's project root |

Migration report

After evaluation, a file named scaledown-report.md is saved in your project root. It follows a fixed schema (version "1") and contains:

| Section | Content | |---|---| | Summary table | Files scanned, files with AI calls, total opportunities, providers detected | | Complexity breakdown | Count of calls at each complexity level (trivial → highly_complex) | | Per-opportunity sections | Files, line numbers, complexity score, confidence level, and action for each of sd_classify / sd_extract / sd_summarize / sd_compress | | Complex call decompositions | For calls scoring 3+/5, a step-by-step breakdown showing which sub-tasks can move to a ScaleDown SLM and which still need a frontier LLM | | HTTP API reference | Exact request/response shapes for all four ScaleDown endpoints (/v1/classify, /v1/extract, /v1/compress, /v1/summarize) |

The report header looks like:

# ScaleDown Migration Plan — my-app

> Generated: 2025-05-19T10:00:00.000Z
> Report version: 1

## Summary
| Metric | Value |
|---|---|
| Files scanned | 42 |
| Files with AI API calls | 7 |
| Total integration opportunities | 11 |
| Providers detected | openai, langchain |

### Call Complexity Breakdown
| Complexity | Count |
|---|---|
| simple | 4 |
| moderate | 2 |
| complex | 1 |

Supported providers

| Provider | Language | |---|---| | OpenAI (v0 and v1) | Python, TypeScript/JS | | Anthropic | Python, TypeScript/JS | | LangChain | Python, TypeScript/JS | | LlamaIndex | Python | | Cohere | Python | | Google Generative AI | Python | | Vercel AI SDK | TypeScript/JS |

Example output

## ScaleDown Migration Plan — my-app

| Metric | Value |
|---|---|
| Files with AI API calls | 7 |
| Total integration opportunities | 11 |
| Providers detected | openai, langchain |

## 1. Replace Classification Calls with `sd_classify`
Estimated savings: ~95% per call

| File | Line |
|---|---|
| `src/triage.py` | 42 |
| `src/router.py` | 88 |

## 2. Add Context Compression with `sd_compress`
Estimated savings: 50-70% on context tokens

| File | Line |
|---|---|
| `src/rag_pipeline.py` | 117 |
| `src/chat.py` | 55 |

Development

git clone https://github.com/scaledown-team/SLM_Agent
cd SLM_Agent
npm install
npm run build       # compile TypeScript → dist/
npm run dev         # run without compiling (via tsx)

Get a ScaleDown API key

Sign up at scaledown.ai/dashboard — 50 million free tokens included.

Extending to other IDEs

• Codex (OpenAI): Add to mcp_servers in your Codex config once MCP support is available
• JetBrains: Use any MCP proxy plugin and point it at npx @scaledown/migration-agent
• Neovim / Emacs: Use any MCP client plugin with the same npx command