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lilbee

lilbee

by tobocop2
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

model_rm

Remove an installed native GGUF model from your local library. Delete models downloaded by lilbee to free up storage; Ollama and LM Studio models remain read-only.

Instructions

Remove an installed model. Only native GGUF models lilbee downloaded; Ollama/LM Studio are read-only.

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": "<model>"
    },
    "name": "model_rm"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelYes
sourceNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.0.3
  2. Removedv1.0.2
  3. First observedv1.0.0

TDQS

A3.5/5.0
Behavior3/5

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 discloses the destructive nature (remove) and the limitation to certain sources, but lacks details on whether removal is irreversible, permissions needed, or any confirmation steps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is very concise at two sentences, front-loading the purpose. It earns its keep by covering scope and limitations, but could be slightly more structured for clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple nature of the tool (removal with two params) and presence of output schema, the description covers the core action and constraints. However, it lacks parameter descriptions, which is a notable gap for completeness.

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

Parameters1/5

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

Schema has 0% description coverage and the description adds no explanation for the 'model' and 'source' parameters. The description does not clarify what values are expected or how 'source' affects behavior, leaving a significant gap.

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?

Description clearly states 'Remove an installed model' with a specific verb and resource. It distinguishes from siblings by specifying it only works on native GGUF models from 'lilbee' (likely a typo for 'libbee' or similar), and notes that Ollama/LM Studio models are read-only.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear context for when to use (removing models) and an exclusion (Ollama/LM Studio models are read-only). However, it does not mention alternatives for removing other model types or any prerequisites.

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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