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Simba MCP Server

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Python
Hybrid

Delete Model

delete_model
DestructiveIdempotent

Permanently delete a failed model and its stored artifacts to clean up incomplete fits. Deleting is irreversible and works only for models with 'failed' status, protecting valid models.

Instructions

PERMANENTLY DELETE a FAILED model. Destructive and irreversible.

Only models with status "failed" can be deleted over the API — any other status returns a 409 with the model's current status (delete is for cleaning up failed fits, not curating good ones). Deleting also unlinks any MMMs that pointed at it as their VAR model and removes stored artifacts. On success returns {"deleted_model_hash": ..., "status": "deleted"}.

Check first with get_model or get_model_status if unsure of the status.

Args: model_hash: Hash of the FAILED model to delete permanently.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_hashYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.5.0
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "title": "delete_modelDictOutput",
+  "type": "object"
+}
  2. First observedv0.3.2

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already mark this destructive and non-read-only, but the description adds meaningful context: deleting is irreversible, unlinks MMMs pointing to the model, removes stored artifacts, and returns a specific success payload. It also discloses the 409 error behavior for non-failed models. No contradiction with annotations exists.

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 dense but every sentence earns its place: scope, irreversibility, precondition, side effects, return value, and pre-check advice are all included. The most critical warning is front-loaded, and the Args section is clean.

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

Completeness5/5

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

For a destructive one-parameter API, the description covers allowed inputs, error conditions, side effects, success response, and recommended pre-checks. Even with an output schema present, it explicitly shows the success payload, leaving little for an agent to guess.

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?

With 0% schema description coverage, the description carries the full burden. It defines model_hash as the hash of the FAILED model to delete permanently, adding the critical status constraint and purpose beyond the schema's bare 'Model Hash' title. It could mention how to obtain the hash or confirm its format, but for a single parameter this is adequate.

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 states a specific verb and resource ('PERMANENTLY DELETE a FAILED model') and clearly scopes the operation to failed models only. This distinguishes it from sibling tools like get_model, rename_model, or unlink_var_model, even without inspecting their schemas.

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

Usage Guidelines5/5

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

The description explicitly says when to use the tool: only for failed models, and explicitly states that any other status returns a 409. It also advises checking with get_model or get_model_status first, giving an agent clear decision guidance.

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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