@voxell/forge-mcp
OfficialServer Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| FORGE_API_KEY | Yes | Forge API key from Voxell dashboard | |
| FORGE_BASE_URL | No | Base URL for the Forge API | https://api.voxell.ai |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| embedA | Generate vector embeddings for one or more texts with Forge (Voxell's hosted embedding API). Use it to turn text into vectors for semantic search, RAG, clustering, or similarity. Set input_type='query' for search queries and 'document' for content you index. Choose model by quality/cost: turbo (1024d, fast, default) -> pro (2560d) -> ultra (4096d, highest quality). Optionally set dim to truncate (Matryoshka, re-normalized). |
| list_modelsA | List the available Forge embedding models and their dimensions. Call this to pick a model before embedding. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
embed and list_models have clearly distinct purposes: one generates vector embeddings, the other lists available models. There is no overlap or ambiguity between them.
Both names use snake_case, but 'embed' is a bare verb while 'list_models' follows a verb_noun pattern. This is a minor deviation from a fully consistent convention.
With only 2 tools, the set feels thin relative to the typical 3-15 range. However, for a narrowly scoped embedding API, each tool earns its place, so it is borderline rather than mismatched.
The surface covers the core embedding workflow: generating embeddings with configurable model and dimension, plus discovering available models. Minor gaps exist (e.g., no batch job management or usage statistics), but the essential operations are present.