The Unsloth MCP Server enables efficient LLM fine-tuning and inference, leveraging Unsloth optimizations for 2x faster training and 80% less memory usage.
Check Installation (check_installation): Verify whether Unsloth is properly installed on your system.
List Supported Models (list_supported_models): Retrieve all models supported by Unsloth, including Llama, Mistral, Phi, and Gemma variants.
Load a Model (load_model): Load a pretrained model with options for 4-bit quantization, custom sequence length, and gradient checkpointing.
Fine-tune a Model (finetune_model): Fine-tune using LoRA/QLoRA on a custom dataset, with configurable learning rate, batch size, LoRA rank/alpha, and gradient accumulation steps.
Generate Text (generate_text): Run inference with a fine-tuned model, controlling output via max tokens, temperature, and top-p sampling.
Export a Model (export_model): Export fine-tuned models to deployment-ready formats — GGUF, Ollama, vLLM, or Hugging Face — with optional quantization settings.
Enables loading, fine-tuning, and using models from Hugging Face, with optional authentication via HUGGINGFACE_TOKEN for accessing private models and datasets.
Requires Node.js for running the MCP server and handling API requests to the Unsloth optimization library.
Utilizes NVIDIA GPUs with CUDA support for accelerated model training and inference, with custom CUDA kernels for performance optimization.
Supports exporting fine-tuned models to Ollama format for local deployment and inference.
Uses OpenAI's Triton language for custom CUDA kernels that optimize model performance.
Leverages Python for model training and inference operations, with specific version requirements (3.10-3.12) for compatibility.
Uses PyTorch as the underlying framework for model operations, with optimizations for faster training and reduced memory usage.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Unsloth MCP Server load the Llama-3.2-1B model with 4-bit quantization for fine-tuning"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP server for Unsloth - a library that makes LLM fine-tuning 2x faster with 80% less memory.
Unsloth is a library that dramatically improves the efficiency of fine-tuning large language models:
Speed: 2x faster fine-tuning compared to standard methods
Memory: 80% less VRAM usage, allowing fine-tuning of larger models on consumer GPUs
Context Length: Up to 13x longer context lengths (e.g., 89K tokens for Llama 3.3 on 80GB GPUs)
Accuracy: No loss in model quality or performance
Unsloth achieves these improvements through custom CUDA kernels written in OpenAI's Triton language, optimized backpropagation, and dynamic 4-bit quantization.
Related MCP server: TOON MCP Server
Optimize fine-tuning for Llama, Mistral, Phi, Gemma, and other models
4-bit quantization for efficient training
Extended context length support
Simple API for model loading, fine-tuning, and inference
Export to various formats (GGUF, Hugging Face, etc.)
Current state: install from source (below). The package is prepared for npm (
name,bin,server.jsonregistry manifest are in place) but is not yet published — thenpx-based config in the next section works only after the npm publish lands.
Install Unsloth on the machine that will run the fine-tuning: pip install unsloth
Build the server from source:
git clone https://github.com/OtotaO/unsloth-mcp-server.git
cd unsloth-mcp-server
npm install
npm run buildPoint your MCP client (Claude Desktop, Claude Code, Cline, Cursor, …) at the built entrypoint:
{
"mcpServers": {
"unsloth": {
"command": "node",
"args": ["/path/to/unsloth-mcp-server/build/index.js"],
"env": { "HUGGINGFACE_TOKEN": "your_token_here" }
}
}
}HUGGINGFACE_TOKEN is optional (only needed for gated/private models). Omit the env block if you don't use one.
Once published to npm as unsloth-mcp-server, the same clients can run it over stdio via npx with no clone/build:
{
"mcpServers": {
"unsloth": {
"command": "npx",
"args": ["-y", "unsloth-mcp-server"],
"env": { "HUGGINGFACE_TOKEN": "your_token_here" }
}
}
}For Claude Code: claude mcp add unsloth -- npx -y unsloth-mcp-server
Verify if Unsloth is properly installed on your system.
Parameters: None
Example:
const result = await use_mcp_tool({
server_name: "unsloth",
tool_name: "check_installation",
arguments: {}
});Get a list of all models supported by Unsloth, including Llama, Mistral, Phi, and Gemma variants.
Parameters: None
Example:
const result = await use_mcp_tool({
server_name: "unsloth",
tool_name: "list_supported_models",
arguments: {}
});Load a pretrained model with Unsloth optimizations for faster inference and fine-tuning.
Parameters:
model_name (required): Name of the model to load (e.g., "unsloth/Llama-3.2-1B")
max_seq_length (optional): Maximum sequence length for the model (default: 2048)
load_in_4bit (optional): Whether to load the model in 4-bit quantization (default: true)
use_gradient_checkpointing (optional): Whether to use gradient checkpointing to save memory (default: true)
Example:
const result = await use_mcp_tool({
server_name: "unsloth",
tool_name: "load_model",
arguments: {
model_name: "unsloth/Llama-3.2-1B",
max_seq_length: 4096,
load_in_4bit: true
}
});Fine-tune a model with Unsloth optimizations using LoRA/QLoRA techniques.
Parameters:
model_name (required): Name of the model to fine-tune
dataset_name (required): Name of the dataset to use for fine-tuning
output_dir (required): Directory to save the fine-tuned model
max_seq_length (optional): Maximum sequence length for training (default: 2048)
lora_rank (optional): Rank for LoRA fine-tuning (default: 16)
lora_alpha (optional): Alpha for LoRA fine-tuning (default: 16)
batch_size (optional): Batch size for training (default: 2)
gradient_accumulation_steps (optional): Number of gradient accumulation steps (default: 4)
learning_rate (optional): Learning rate for training (default: 2e-4)
max_steps (optional): Maximum number of training steps (default: 100)
dataset_text_field (optional): Field in the dataset containing the text (default: 'text')
load_in_4bit (optional): Whether to use 4-bit quantization (default: true)
Example:
const result = await use_mcp_tool({
server_name: "unsloth",
tool_name: "finetune_model",
arguments: {
model_name: "unsloth/Llama-3.2-1B",
dataset_name: "tatsu-lab/alpaca",
output_dir: "./fine-tuned-model",
max_steps: 100,
batch_size: 2,
learning_rate: 2e-4
}
});Generate text using a fine-tuned Unsloth model.
Parameters:
model_path (required): Path to the fine-tuned model
prompt (required): Prompt for text generation
max_new_tokens (optional): Maximum number of tokens to generate (default: 256)
temperature (optional): Temperature for text generation (default: 0.7)
top_p (optional): Top-p for text generation (default: 0.9)
Example:
const result = await use_mcp_tool({
server_name: "unsloth",
tool_name: "generate_text",
arguments: {
model_path: "./fine-tuned-model",
prompt: "Write a short story about a robot learning to paint:",
max_new_tokens: 512,
temperature: 0.8
}
});Export a fine-tuned Unsloth model to various formats for deployment.
Parameters:
model_path (required): Path to the fine-tuned model
export_format (required): Format to export to (gguf, ollama, vllm, huggingface)
output_path (required): Path to save the exported model
quantization_bits (optional): Bits for quantization (for GGUF export) (default: 4)
Example:
const result = await use_mcp_tool({
server_name: "unsloth",
tool_name: "export_model",
arguments: {
model_path: "./fine-tuned-model",
export_format: "gguf",
output_path: "./exported-model.gguf",
quantization_bits: 4
}
});You can use custom datasets by formatting them properly and hosting them on Hugging Face or providing a local path:
const result = await use_mcp_tool({
server_name: "unsloth",
tool_name: "finetune_model",
arguments: {
model_name: "unsloth/Llama-3.2-1B",
dataset_name: "json",
data_files: {"train": "path/to/your/data.json"},
output_dir: "./fine-tuned-model"
}
});For large models on limited hardware:
Reduce batch size and increase gradient accumulation steps
Use 4-bit quantization
Enable gradient checkpointing
Reduce sequence length if possible
CUDA Out of Memory: Reduce batch size, use 4-bit quantization, or try a smaller model
Import Errors: Ensure you have the correct versions of torch, transformers, and unsloth installed
Model Not Found: Check that you're using a supported model name or have access to private models
Python: 3.10, 3.11, or 3.12 (not 3.13)
CUDA: 11.8 or 12.1+ recommended
PyTorch: 2.0+ recommended
Model | VRAM | Unsloth Speed | VRAM Reduction | Context Length |
Llama 3.3 (70B) | 80GB | 2x faster | >75% | 13x longer |
Llama 3.1 (8B) | 80GB | 2x faster | >70% | 12x longer |
Mistral v0.3 (7B) | 80GB | 2.2x faster | 75% less | - |
Python 3.10-3.12
NVIDIA GPU with CUDA support (recommended)
Node.js and npm
Apache-2.0
Check if Unsloth is properly installed
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't describe what 'properly installed' means, what checks are performed, what output format to expect, or whether this has side effects. For a diagnostic tool with zero annotation coverage, this leaves significant behavioral questions unanswered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose with zero wasted words. It's appropriately sized for a simple diagnostic tool and front-loads the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter diagnostic tool with no output schema, the description states the basic purpose adequately. However, it doesn't explain what constitutes 'properly installed' or what format the result will take, leaving the agent uncertain about how to interpret the tool's output. Given the simplicity of the tool, this is minimally viable but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, and the schema already fully documents this.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: checking if Unsloth is properly installed. It uses a specific verb ('check') and identifies the target resource ('Unsloth installation'). However, it doesn't differentiate from siblings like 'list_supported_models' which might also involve installation status checks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, timing considerations, or relationships to sibling tools like 'list_supported_models' or 'load_model' that might be used before or after installation checks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Export a fine-tuned Unsloth model to various formats
| Name | Required | Description | Default |
|---|---|---|---|
| model_path | Yes | Path to the fine-tuned model | |
| export_format | Yes | Format to export to (gguf, ollama, vllm, huggingface) | |
| output_path | Yes | Path to save the exported model | |
| quantization_bits | No | Bits for quantization (for GGUF export) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool exports models but doesn't describe what the export entails (e.g., file creation, format conversion, potential data loss, permissions required, or rate limits). For a tool with 4 parameters and no annotations, this leaves significant gaps in understanding its behavior and side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose ('Export a fine-tuned Unsloth model') and adds necessary detail ('to various formats'). There is no wasted verbiage, and it's appropriately sized for the tool's complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters with full schema coverage but no annotations and no output schema, the description is minimally adequate. It covers the basic purpose but lacks behavioral context, usage guidelines, and output details. For a tool that likely involves file operations and format conversions, more completeness would be beneficial, but it meets a bare minimum.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'various formats,' which aligns with the 'export_format' enum but doesn't provide additional syntax or usage details. Baseline 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Export') and resource ('a fine-tuned Unsloth model'), specifying the target formats ('various formats'). It distinguishes from siblings like 'finetune_model' or 'load_model' by focusing on export rather than creation or loading. However, it doesn't explicitly differentiate from all siblings (e.g., 'generate_text' is clearly different, but the distinction is implicit rather than explicit).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a fine-tuned model first), exclusions (e.g., not for raw models), or comparisons to sibling tools like 'list_supported_models' for checking export options. Usage is implied by the action but lacks explicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fine-tune a model with Unsloth optimizations
| Name | Required | Description | Default |
|---|---|---|---|
| model_name | Yes | Name of the model to fine-tune | |
| dataset_name | Yes | Name of the dataset to use for fine-tuning | |
| output_dir | Yes | Directory to save the fine-tuned model | |
| max_seq_length | No | Maximum sequence length for training | |
| lora_rank | No | Rank for LoRA fine-tuning | |
| lora_alpha | No | Alpha for LoRA fine-tuning | |
| batch_size | No | Batch size for training | |
| gradient_accumulation_steps | No | Number of gradient accumulation steps | |
| learning_rate | No | Learning rate for training | |
| max_steps | No | Maximum number of training steps | |
| dataset_text_field | No | Field in the dataset containing the text | |
| load_in_4bit | No | Whether to use 4-bit quantization |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'Unsloth optimizations' but doesn't explain what this entails (e.g., performance improvements, memory efficiency). Critical behavioral traits like computational cost, time requirements, potential data destruction, or output format are omitted. The description adds minimal context beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It is front-loaded with the core action ('fine-tune a model') and includes a relevant detail ('with Unsloth optimizations') that adds value without verbosity. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a fine-tuning operation (12 parameters, no annotations, no output schema), the description is incomplete. It lacks information on behavioral aspects (e.g., runtime, resource usage), output (what is returned), and usage guidelines. The high parameter count and absence of structured support fields make this inadequate for such a complex tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 12 parameters thoroughly (e.g., 'model_name', 'dataset_name', 'lora_rank'). The description adds no additional meaning about parameters, such as typical values, constraints, or relationships between them. Baseline score of 3 is appropriate as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('fine-tune') and resource ('a model') with a specific optimization method ('with Unsloth optimizations'). It distinguishes from siblings like 'export_model' or 'generate_text' by focusing on training rather than inference or export. However, it doesn't explicitly differentiate from 'load_model' in terms of when to use each.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a pre-trained model and dataset), when not to use it (e.g., for inference tasks), or refer to sibling tools like 'list_supported_models' for model selection. Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate text using a fine-tuned Unsloth model
| Name | Required | Description | Default |
|---|---|---|---|
| model_path | Yes | Path to the fine-tuned model | |
| prompt | Yes | Prompt for text generation | |
| max_new_tokens | No | Maximum number of tokens to generate | |
| temperature | No | Temperature for text generation | |
| top_p | No | Top-p for text generation |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden but only states the basic function without disclosing behavioral traits like computational cost, rate limits, error handling, or output format. It mentions 'fine-tuned Unsloth model' but doesn't explain implications for performance or compatibility.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, with every part contributing essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a text generation tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It fails to address key aspects like what the output looks like, error conditions, or how it integrates with sibling tools (e.g., requiring 'load_model' first).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 5 parameters. The description adds no additional meaning beyond what's in the schema, such as typical values for 'temperature' or 'top_p', or how 'model_path' relates to other tools. Baseline 3 is appropriate as the schema handles parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Generate text') and specifies the resource ('using a fine-tuned Unsloth model'), which distinguishes it from siblings like 'finetune_model' or 'export_model'. However, it doesn't explicitly differentiate from 'load_model' in terms of when to use each for text generation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like 'load_model' (which might be a prerequisite) or other text generation methods. The description lacks context about prerequisites, such as needing a loaded model, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all models supported by Unsloth
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states what the tool does but lacks behavioral details such as whether it requires authentication, has rate limits, returns paginated results, or what format the output takes. This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It is appropriately sized and front-loaded, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. It explains what the tool does but lacks context on output format, behavioral traits, or usage guidelines. This makes it complete enough for basic understanding but with clear gaps in practical application.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it appropriately doesn't mention any. A baseline of 4 is applied as per the rules for zero parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('all models supported by Unsloth'), providing a specific purpose. However, it doesn't differentiate from sibling tools like 'check_installation' or 'load_model' which might also involve model information, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. Sibling tools like 'check_installation' or 'load_model' might overlap in context, but the description doesn't mention any prerequisites, exclusions, or specific scenarios for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Load a pretrained model with Unsloth optimizations
| Name | Required | Description | Default |
|---|---|---|---|
| model_name | Yes | Name of the model to load (e.g., "unsloth/Llama-3.2-1B") | |
| max_seq_length | No | Maximum sequence length for the model | |
| load_in_4bit | No | Whether to load the model in 4-bit quantization | |
| use_gradient_checkpointing | No | Whether to use gradient checkpointing to save memory |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'Unsloth optimizations' but doesn't explain what this entails (e.g., memory efficiency, speed improvements) or potential side effects (e.g., memory usage, time to load, compatibility issues). It lacks details on error handling, performance implications, or what 'load' means operationally (e.g., into memory for subsequent use).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It front-loads the core action and key detail ('Unsloth optimizations'), making it easy to parse. Every word earns its place without redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (loading models with optimizations), lack of annotations, and no output schema, the description is incomplete. It doesn't cover what happens after loading (e.g., model availability for other tools), potential errors, or the impact of Unsloth optimizations. For a tool with 4 parameters and no structured safety or output info, more context is needed to guide effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no additional meaning beyond what's in the schema (e.g., it doesn't clarify the purpose of 'max_seq_length' or trade-offs for 'load_in_4bit'). Baseline score of 3 is appropriate as the schema handles parameter documentation adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Load') and resource ('a pretrained model'), and specifies the optimization framework ('with Unsloth optimizations'). It distinguishes from siblings like 'finetune_model' (training) and 'list_supported_models' (listing), but doesn't explicitly differentiate from 'export_model' or 'generate_text' in terms of loading vs. using the model.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., after checking installation with 'check_installation'), when loading is needed (e.g., before fine-tuning or generation), or what happens if the model is already loaded. The description assumes context without explicit direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
check_installationexport_modelfinetune_modelgenerate_textlist_supported_modelsload_modelEach 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.
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