Forge

MCP server for Forge, Voxell's embedding API. Voxell's Ingot-8B-R3 ranks #1 for English on MTEB.

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Transport
Not stated
Package
—
Registry id
ai.voxell/forge

No install snippet on purpose. A working MCP config is a command, its arguments and an environment block — the last two are where API keys live, so this catalogue never stores them and cannot publish them. Follow the link above for the authors' own instructions.

mcp

An MCP server for Forge — Voxell's hosted text-embedding API. It exposes Forge to any MCP client (Claude, Cursor, Cline, Windsurf, VS Code, …) as two tools:

  • embed — turn text into vectors
  • list_models — list available models and their dimensions

You bring a Forge API key. The server is stateless, and Voxell does not store the text you send or the vectors it returns — only usage metadata (token counts) is recorded, for billing. It does embeddings only — no storage, no search, no RAG. Those are different products.

Quick install

One-click install in your editor (then replace your-key-here with a real key from dash.voxell.ai):

Add to Cursor Install in VS Code

Claude Code — one command:

claude mcp add forge -e FORGE_API_KEY=your-key-here -- npx -y @voxell/forge-mcp

Any other client (Claude Desktop, Cline, Windsurf, Zed, …) uses the standard mcpServers block — see Use it below.

Why Forge

  • Quality you can dial. Three tiers: turbo (1024d, fast, the default), pro (2560d) and ultra (4096d, highest quality). Pick your point on the quality and cost curve.
  • Benchmark-leading. Voxell's Ingot-8B-R3 ranks #1 for English on the public MTEB leaderboard (English v2), with a 75.98 mean task score across 41 tasks: the top usable English embedding model. See the model card.
  • Matryoshka (MRL). Set dim to truncate (re-normalized) for ~4× smaller, cheaper vectors.
  • Low latency (Go + CUDA engine), zero-trust (per-key auth; mTLS available), and free to start (turbo is free forever, no card: dash.voxell.ai; more at voxell.ai/forge).

What you can do with it

  • Add semantic search — embed your documents with input_type: "document" and each query with input_type: "query", then rank by cosine similarity.
  • Build RAG — embed a knowledge base, store the vectors, and retrieve the closest chunks to ground an LLM.
  • Find similar or duplicate text — embed two texts and compare their vectors.
  • Cluster or classify — embed a batch, then cluster or train a classifier on the vectors.
  • Shrink vector storage — set dim to truncate (Matryoshka) and trade a little accuracy for smaller, cheaper vectors.
  • Straight from your editor — ask your AI agent (Cursor, Claude, …) to embed a snippet, a batch, or a file via the embed tool — no separate script.

Requirements

  • Node.js ≥ 18 (tested on 20)
  • A Forge API key — create one at https://dash.voxell.ai. New accounts start with 10M free tokens, no credit card.

Use it

Most MCP clients run it on demand with npx. Add this to your client's MCP config:

{
  "mcpServers": {
    "forge": {
      "command": "npx",
      "args": ["-y", "@voxell/forge-mcp"],
      "env": { "FORGE_API_KEY": "your-key-here" }
    }
  }
}

(Cursor, Claude Desktop, Cline, Windsurf, and VS Code all use this mcpServers shape.)

Tools

embed

From the project's README.

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