ai.voxell/forge

@voxell/forge-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):

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.
• Measured on public documents. Voxell's retrieval pipeline, built on Forge, is measured on four public corpora (7,817 documents, 980,885 passages). On 800 model-written questions, which are easier than a test set written by people, the first result answers the question for 83%. Method and limits: Measured retrieval.
• 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).

Measured retrieval

This server returns vectors only. Voxell also runs a retrieval pipeline built on Forge, and publishes what it measures on documents anyone can read.

Four public corpora, 7,817 documents and 980,885 passages in total, 800 questions (200 per corpus), measured 2026-10-05:

The right document is in the top ten for 95% of the questions.

Read these numbers for what they are. The questions were written by a model from the documents and judged against the passage text, which is easier than a test set written by people. The numbers describe what this pipeline does on these corpora. They are not a comparison with any other vendor, and they are not what the embed tool does on its own: the pipeline adds the steps the receipts list, such as a cross-encoder reranker, a one-line document identity in every passage, and routing to the documents a question names. Each receipt is published with a fixed sample of its questions, misses included: voxell.ai/retrieval.

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. turbo is free forever, no 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

Returns the vectors plus the model, dimension, and token count.

Default is turbo — the one you probably want. pro/ultra trade size and speed for more dimensions.

list_models

Lists the available models and their dimensions.

Configuration

Beyond MCP: OpenAI-compatible API

Forge speaks the OpenAI embeddings API. Point any OpenAI client at Forge — no code change, and your existing vector dimensions are preserved:

from openai import OpenAI

client = OpenAI(base_url="https://api.voxell.ai/v1", api_key="your-forge-key")
# the exact call you already make — now on a higher-ranked engine:
client.embeddings.create(model="text-embedding-3-large", input=["hello world"])  # -> 3072-d

Your OpenAI model names map to a matching-dimension Forge tier (text-embedding-3-small/ ada-002 → 1536-d, text-embedding-3-large → 3072-d), so existing vector stores slot in unchanged. Or address Forge tiers directly — turbo | pro | ultra. Also supports dimensions (Matryoshka, re-normalized) and encoding_format: "base64".

It's an upgrade on every path. Forge's smallest tier (turbo) outranks OpenAI's largest embedding model (text-embedding-3-large) on MTEB, so there's no drop-in that lands worse. And Voxell's Ingot-8B-R3 ranks #1 for English on the public MTEB leaderboard: a different league.

Why re-embedding onto Forge is worth it. Embedding is a one-way door: whatever an encoder discards at write time is gone — no reranker, longer prompt, or bigger LLM downstream reconstructs what the vectors never captured. The model you embed with sets the ceiling on everything above it. Re-embed once onto a higher-ranked engine and that ceiling rises — permanently.

License

MIT © Voxell, Inc.