VoxellInc/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:
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:
| corpus | documents | first result answers the question | one of the top three answers it |
|--------|-----------|-----------------------------------|---------------------------------|
| SEC filings | 2,010 | 91% | 95% |
| USPTO patents | 4,008 | 86.5% | 91.5% |
| NASA technical reports | 1,210 | 67% | 78% |
| arXiv technical papers | 589 | 86.5% | 96% |
| all four | 7,817 | 83% | 90% |
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