@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.

Related MCP server: OpenRouter MCP Server

Why Forge

• Quality you can dial. Forge runs the Qwen3-Embedding family; ultra is the 8B — ~75+ average task score on MTEB, currently #4 on MTEB (English), and the top usable model (the three ranked above it are research-only). turbo (0.6B) is the fast/cheap default. Pick your quality/cost point.

• 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 (10M tokens, 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

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, Qwen3-Embedding-0.6B) outranks OpenAI's largest embedding model (text-embedding-3-large) on MTEB — so there's no drop-in that lands worse. ultra (Qwen3-Embedding-8B, ~75+ average task score, #4 on MTEB English) is 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.