Cerebras Multi Mcp — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Cerebras Multi Mcp (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Use multiple Cerebras models from Claude Desktop & Claude Code — with automatic model selection.
The official Cerebras MCP package only supports one model per session — you pick a model via an environment variable, and you're stuck with it until you restart. Want to use the fast 8B model for boilerplate and the 357B model for complex reasoning? You'd need two separate MCP server configs.
cerebras-multi-mcp exposes 5 tools — one for each Cerebras model plus an auto-selector — so you (or Claude) can pick the right model per task, in the same session, with zero restarts.
┌─────────────────────────────────────────────────┐
│ Claude Desktop / Code │
├─────────────────────────────────────────────────┤
│ │
│ cerebras_quick → llama3.1-8b (8B) │
│ cerebras_complex → gpt-oss-120b (120B) │
│ cerebras_reasoning → zai-glm-4.7 (357B) │
│ cerebras_instruct → qwen-3-235b (235B) │
│ cerebras_auto → picks the best one │
│ │
├─────────────────────────────────────────────────┤
│ Cerebras API ←→ OpenRouter Fallback │
└─────────────────────────────────────────────────┘| Tool | Model | Params | Best For |
|---|---|---|---|
cerebras_quick | llama3.1-8b | 8B | Simple edits, boilerplate, single functions. Fastest. |
cerebras_complex | gpt-oss-120b | 120B | Multi-file features, CRUD APIs, complex components. |
cerebras_reasoning | zai-glm-4.7 | 357B | Algorithms, architecture, advanced logic, deep reasoning. |
cerebras_instruct | qwen-3-235b | 235B | Precise instructions, documentation, typed interfaces, specs. |
cerebras_auto | auto-selected | — | Analyzes your prompt and picks the best model automatically. |
cerebras_auto analyzes your prompt keywords and complexity:
git clone https://github.com/khansabassem/cerebras-multi-mcp.git
cd cerebras-multi-mcp
npm installEdit your Claude Desktop config file:
~/Library/Application Support/Claude/claude_desktop_config.json%APPDATA%\Claude\claude_desktop_config.jsonAdd the cerebras-multi entry:
{
"mcpServers": {
"cerebras-multi": {
"command": "node",
"args": ["<path-to>/cerebras-multi-mcp/src/index.js"],
"env": {
"CEREBRAS_API_KEY": "your-cerebras-api-key",
"OPENROUTER_API_KEY": "your-openrouter-api-key"
}
}
}
}Restart Claude Desktop to load the new server.
claude mcp add cerebras-multi \
-e CEREBRAS_API_KEY=your-cerebras-api-key \
-e OPENROUTER_API_KEY=your-openrouter-api-key \
-- node /path/to/cerebras-multi-mcp/src/index.jsOnce configured, you'll see 5 new tools in Claude. Each tool accepts:
| Parameter | Required | Description |
|---|---|---|
file_path | Yes | Absolute path to the file to create or modify |
prompt | Yes | Detailed code generation instructions |
context_files | No | Array of file paths to read as context |
temperature | No | Sampling temperature (default: 0.1) |
max_tokens | No | Maximum tokens in the response |
Quick boilerplate with the 8B model:
Tool: cerebras_quick
file_path: /project/src/server.js
prompt: Create an Express server with health check endpoint on port 3000Complex feature with the 120B model:
Tool: cerebras_complex
file_path: /project/src/auth/middleware.ts
prompt: Create JWT authentication middleware with refresh token rotation
context_files: ["/project/src/types/auth.ts", "/project/src/config/env.ts"]Algorithm design with the 357B model:
Tool: cerebras_reasoning
file_path: /project/src/utils/graph.ts
prompt: Implement Dijkstra's shortest path with a priority queue, supporting weighted directed graphsDocumentation with the 235B model:
Tool: cerebras_instruct
file_path: /project/src/types/api.ts
prompt: Generate TypeScript interfaces for a REST API with OpenAPI-compatible JSDoc annotationsLet the server decide:
Tool: cerebras_auto
file_path: /project/src/cache.ts
prompt: Build an LRU cache with O(1) get and put using a doubly linked listsrc/index.js — Single-file MCP server (~350 lines)
├── Config — Model definitions, keyword lists, language detection
├── File helpers — Safe read/write with path resolution
├── HTTP layer — Cerebras API + OpenRouter fallback
├── Auto-selector — Keyword-based model routing
├── Tool handler — Unified handler for all 5 tools
└── MCP server — ListTools + CallTool with schema factoryBuilt with @modelcontextprotocol/sdk using stdio transport.
Cerebras inference runs on purpose-built wafer-scale hardware, delivering up to 20x faster inference than traditional GPU setups. Combined with MCP, you get near-instant code generation directly inside Claude.
Bassem EL KHANSAA — @ask.bassem
MIT
Issues and PRs welcome. If you add a new model, just extend the MODELS object and add a tool entry in the ListToolsRequestSchema handler.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.