@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, #4 on MTEB English, top usable). 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
The two tools have entirely distinct purposes: embedding text and listing models. There is no overlap or ambiguity.
While 'embed' is a single verb and 'list_models' follows verb_noun pattern, the naming is still clear and predictable. Minor inconsistency but not confusing.
With only 2 tools, the server feels minimal. However, for a focused embedding API, this may be sufficient. It is borderline but not extreme.
The tool set covers the core operations for an embedding service: generating embeddings and listing available models. No obvious gaps for the stated purpose.