Unsloth 용 MCP 서버 - LLM 미세 조정을 80% 적은 메모리로 2배 더 빠르게 만드는 라이브러리입니다.
Unsloth는 대규모 언어 모델의 미세 조정 효율성을 획기적으로 향상시키는 라이브러리입니다.
속도 : 표준 방식 대비 2배 빠른 미세 조정
메모리 : VRAM 사용량이 80% 감소하여 소비자용 GPU에서 더 큰 모델의 미세 조정이 가능
컨텍스트 길이 : 최대 13배 더 긴 컨텍스트 길이(예: 80GB GPU에서 Llama 3.3의 경우 89K 토큰)
정확도 : 모델 품질이나 성능 저하 없음
Unsloth는 OpenAI의 Triton 언어로 작성된 맞춤형 CUDA 커널, 최적화된 역전파, 동적 4비트 양자화를 통해 이러한 개선을 달성합니다.
Related MCP server: TOON MCP Server
Llama, Mistral, Phi, Gemma 및 기타 모델에 대한 미세 조정 최적화
효율적인 학습을 위한 4비트 양자화
확장된 컨텍스트 길이 지원
모델 로딩, 미세 조정 및 추론을 위한 간단한 API
다양한 포맷(GGUF, Hugging Face 등)으로 내보내기
Unsloth 설치: pip install unsloth
서버를 설치하고 빌드합니다.
지엑스피1
MCP 설정에 추가:
{
"mcpServers": {
"unsloth-server": {
"command": "node",
"args": ["/path/to/unsloth-server/build/index.js"],
"env": {
"HUGGINGFACE_TOKEN": "your_token_here" // Optional
},
"disabled": false,
"autoApprove": []
}
}
}Unsloth가 시스템에 제대로 설치되었는지 확인하세요.
매개변수 : 없음
예 :
const result = await use_mcp_tool({
server_name: "unsloth-server",
tool_name: "check_installation",
arguments: {}
});Llama, Mistral, Phi, Gemma 변형을 포함하여 Unsloth가 지원하는 모든 모델 목록을 받아보세요.
매개변수 : 없음
예 :
const result = await use_mcp_tool({
server_name: "unsloth-server",
tool_name: "list_supported_models",
arguments: {}
});더 빠른 추론과 미세 조정을 위해 Unsloth 최적화를 적용한 사전 학습된 모델을 로드합니다.
매개변수 :
model_name (필수): 로드할 모델의 이름(예: "unsloth/Llama-3.2-1B")
max_seq_length (선택 사항): 모델의 최대 시퀀스 길이(기본값: 2048)
load_in_4bit (선택 사항): 모델을 4비트 양자화로 로드할지 여부(기본값: true)
use_gradient_checkpointing (선택 사항): 메모리를 절약하기 위해 그래디언트 체크포인팅을 사용할지 여부(기본값: true)
예 :
const result = await use_mcp_tool({
server_name: "unsloth-server",
tool_name: "load_model",
arguments: {
model_name: "unsloth/Llama-3.2-1B",
max_seq_length: 4096,
load_in_4bit: true
}
});LoRA/QLoRA 기술을 사용하여 Unsloth 최적화로 모델을 미세 조정합니다.
매개변수 :
model_name (필수): 미세 조정할 모델의 이름
dataset_name (필수): 미세 조정에 사용할 데이터 세트의 이름
output_dir (필수): 미세 조정된 모델을 저장할 디렉토리
max_seq_length (선택 사항): 학습을 위한 최대 시퀀스 길이(기본값: 2048)
lora_rank (선택 사항): LoRA 미세 조정을 위한 순위(기본값: 16)
lora_alpha (선택 사항): LoRA 미세 조정을 위한 알파(기본값: 16)
batch_size (선택 사항): 학습을 위한 배치 크기(기본값: 2)
gradient_accumulation_steps (선택 사항): 그래디언트 축적 단계 수(기본값: 4)
learning_rate (선택 사항): 학습을 위한 학습 속도(기본값: 2e-4)
max_steps (선택 사항): 최대 학습 단계 수(기본값: 100)
dataset_text_field (선택 사항): 텍스트가 포함된 데이터 세트의 필드(기본값: 'text')
load_in_4bit (선택 사항): 4비트 양자화를 사용할지 여부(기본값: true)
예 :
const result = await use_mcp_tool({
server_name: "unsloth-server",
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
}
});정교하게 조정된 Unsloth 모델을 사용하여 텍스트를 생성합니다.
매개변수 :
model_path (필수): 미세 조정된 모델에 대한 경로
prompt (필수): 텍스트 생성을 위한 프롬프트
max_new_tokens (선택 사항): 생성할 최대 토큰 수(기본값: 256)
temperature (선택 사항): 텍스트 생성을 위한 온도(기본값: 0.7)
top_p (선택 사항): 텍스트 생성을 위한 Top-p(기본값: 0.9)
예 :
const result = await use_mcp_tool({
server_name: "unsloth-server",
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
}
});미세하게 조정된 Unsloth 모델을 다양한 형식으로 내보내 배포합니다.
매개변수 :
model_path (필수): 미세 조정된 모델에 대한 경로
export_format (필수): 내보낼 형식(gguf, ollama, vllm, huggingface)
output_path (필수): 내보낸 모델을 저장할 경로
quantization_bits (선택 사항): 양자화를 위한 비트(GGUF 내보내기용)(기본값: 4)
예 :
const result = await use_mcp_tool({
server_name: "unsloth-server",
tool_name: "export_model",
arguments: {
model_path: "./fine-tuned-model",
export_format: "gguf",
output_path: "./exported-model.gguf",
quantization_bits: 4
}
});사용자 정의 데이터 세트를 올바르게 포맷하고 Hugging Face에 호스팅하거나 로컬 경로를 제공하여 사용할 수 있습니다.
const result = await use_mcp_tool({
server_name: "unsloth-server",
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"
}
});제한된 하드웨어를 사용하는 대형 모델의 경우:
배치 크기를 줄이고 그래디언트 축적 단계를 늘립니다.
4비트 양자화를 사용하세요
그래디언트 체크포인팅 활성화
가능하다면 시퀀스 길이를 줄이세요
CUDA 메모리 부족 : 배치 크기를 줄이거나 4비트 양자화를 사용하거나 더 작은 모델을 시도하세요.
가져오기 오류 : torch, transformers 및 unsloth의 올바른 버전이 설치되어 있는지 확인하세요.
모델을 찾을 수 없음 : 지원되는 모델 이름을 사용하고 있는지 또는 비공개 모델에 액세스할 수 있는지 확인하세요.
Python: 3.10, 3.11 또는 3.12(3.13 아님)
CUDA: 11.8 또는 12.1+ 권장
PyTorch: 2.0 이상 권장
모델 | 비디오램 | 느림보 속도 | VRAM 감소 | 컨텍스트 길이 |
라마 3.3 (70B) | 80GB | 2배 더 빠름 | >75% | 13배 더 길다 |
라마 3.1 (8B) | 80GB | 2배 더 빠름 | >70% | 12배 더 길다 |
미스트랄 v0.3 (7B) | 80GB | 2.2배 더 빠름 | 75% 감소 | - |
파이썬 3.10-3.12
CUDA를 지원하는 NVIDIA GPU(권장)
Node.js와 npm
아파치-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.
check_installationexport_modelfinetune_modelgenerate_textlist_supported_modelsload_modelScored 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.
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