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Glama
VoxellInc

@voxell/forge-mcp

Official
by VoxellInc

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
FORGE_API_KEYYesForge API key from Voxell dashboard
FORGE_BASE_URLNoBase URL for the Forge APIhttps://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

CapabilityDetails
tools
{
  "listChanged": true
}

Tools

Functions exposed to the LLM to take actions

NameDescription
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

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

A4.1/5.0

Scored across 2 tools

Disambiguation5/5

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.

Naming Consistency4/5

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.

Tool Count3/5

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.

Completeness4/5

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.

Maintenance

ActivityMaintained
ResponsivenessNo issues