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Unsloth MCP Server

Unsloth MCP Server

by OtotaO
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

load_model

Load a pretrained model with Unsloth optimizations for faster training and reduced memory usage, supporting quantization and gradient checkpointing.

Instructions

Load a pretrained model with Unsloth optimizations

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "model_name": "<model_name>"
    },
    "name": "load_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_nameYesName of the model to load (e.g., "unsloth/Llama-3.2-1B")
max_seq_lengthNoMaximum sequence length for the model
load_in_4bitNoWhether to load the model in 4-bit quantization
use_gradient_checkpointingNoWhether to use gradient checkpointing to save memory

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

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.

Conciseness5/5

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.

Completeness2/5

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.

Parameters3/5

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.

Purpose4/5

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.

Usage Guidelines2/5

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.

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