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remove_model

Destructive

Delete a local Ollama model with a recorded undo. Run dry_run first to preview the impact and check whether re-pulling the model is permitted by policy.

Instructions

[WRITE][risk=high] Delete a local model. Destructive — pass dry_run=True to preview.

Captures the model's manifest so the harness records an undo (re-pull).

An undo is recorded only when the allow/deny policy would permit re-pulling the model. For a denied one the result says reversible=false and explains why, rather than recording a re-pull that undo_apply is bound to refuse. The dry-run preview carries the same verdict, so the caller learns it before the deletion rather than after.

Args: model: Model name to delete. dry_run: If True, return what WOULD be deleted without deleting. target: Ollama target name from config; omit for the default.

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": "remove_model"
  }
}

Input Schema

Name	Required	Description	Default
`model`	Yes
`target`	No
`dry_run`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.6.0Jul 29, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Annotations already declare destructiveHint=true and idempotentHint=false, but the description adds substantial context the annotations cannot: undo/re-pull recording, the interaction with the allow/deny policy, the reversible=false outcome for denied models, and that dry-run carries the same verdict. This is exactly the beyond-annotation disclosure an agent needs before a destructive call.

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?

Front-loads the risk marker and destructive warning, which is the right structure for a high-risk tool. The undo-policy explanation spans several sentences and restates the dry-run verdict point, so it is slightly more verbose than needed, but most sentences earn their place.

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

Completeness4/5

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

For a destructive, non-idempotent tool without an output schema, the description covers the critical unknowns: reversibility, undo recording, policy preconditions, and dry-run behavior. It does not detail the actual return payload beyond reversible=false, but the essential decision inputs are present.

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

Parameters4/5

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

Schema description coverage is 0%, so the description carries the full burden and documents all three parameters, including intent for dry_run ('return what WOULD be deleted without deleting') and target ('Ollama target name from config; omit for the default'). Only model is covered tersely, but the semantics are adequately conveyed, so it compensates for the empty schema.

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?

States a specific verb and resource ('Delete a local model') and immediately flags the WRITE/high-risk nature. It is clearly distinguishable from siblings like unload_model (unload vs. permanent delete) and pull_model, so an agent can route correctly.

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?

Gives an explicit when-to-use instruction for the dry_run preview ('pass dry_run=True to preview'), which is actionable guidance. It does not, however, contrast itself against the semantically close sibling unload_model or state when not to use it, so it stops short of full alternative routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.