Unsloth MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| HUGGINGFACE_TOKEN | No | Optional Hugging Face token for accessing private models or higher rate limits. |
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
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| check_installationB | Check if Unsloth is properly installed |
| list_supported_modelsB | List all models supported by Unsloth |
| load_modelC | Load a pretrained model with Unsloth optimizations |
| finetune_modelC | Fine-tune a model with Unsloth optimizations |
| generate_textC | Generate text using a fine-tuned Unsloth model |
| export_modelB | Export a fine-tuned Unsloth model to various formats |
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 6 tools
Each tool has a clearly distinct purpose with no overlap: checking installation, listing models, loading models, fine-tuning, generating text, and exporting models. The descriptions make it unambiguous which tool to use for each step in the workflow.
All tools follow a consistent verb_noun pattern with clear, descriptive names (e.g., check_installation, export_model, finetune_model). There are no deviations in naming conventions or style mixing.
With 6 tools, this server is well-scoped for its purpose of fine-tuning and using models with Unsloth optimizations. Each tool earns its place by covering a distinct, essential operation in the model lifecycle.
The tool set provides complete coverage for the domain: from checking prerequisites and listing models, through loading, fine-tuning, and generating text, to exporting the final model. There are no obvious gaps or dead ends in the workflow.