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Ollama MCP Wrapper

Ollama MCP Wrapper

by PieterVDMerwe
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

stop_model

Free up memory by stopping and unloading a specific model from RAM/VRAM.

Instructions

Stop and unload a specific model from memory (RAM/VRAM).

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": "stop_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_nameYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description is the sole source of behavioral info. It states the action but not side effects (e.g., terminating active completions), prerequisites, or error conditions. The RAM/VRAM mention adds some context, but more is needed for a mutating operation.

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, front-loaded sentence that conveys the essential action without waste. It is appropriately concise for the tool's simplicity.

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?

For a tool with no output schema and no annotations, the description is incomplete. It fails to explain when to use it, what the result or return value is, and how to source model_name. Additional context about the lifecycle of models would improve usability.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema only shows model_name as a string with no description. The description mentions 'a specific model' but does not say how to identify it, where to get valid values, or any format expectations. With 0% schema coverage, the description should compensate but doesn't.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('stop and unload') and identifies the resource ('a specific model') and context ('from memory (RAM/VRAM)'). It clearly distinguishes from sibling tools like list_local_models and run_model_completion, which have different actions.

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?

The description provides no guidance on when to use this tool versus alternatives. It does not mention checking running models first, nor when it is appropriate to unload a model. There are no exclusions or alternatives referenced.

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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