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Simba MCP Server

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by getsimba-ai

delete_model

Permanently delete a failed model to clean up incomplete fits. Requires a model hash and only works for failed models; other statuses are rejected to prevent accidental removal.

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
Behavior5/5

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

With no annotations provided, the description carries the full burden and succeeds: it discloses irreversibility, the 409 error behavior for non-failed models, side effects on linked MMMs, removal of stored artifacts, and the exact success response shape. This is exemplary for a destructive mutation tool.

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?

Every sentence earns its place: the destructive warning is front-loaded, followed by status constraints, side effects, response format, and a pre-check hint. The Args section is minimal and directly tied to the parameter. No filler.

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?

Despite no annotations and no output schema, the description fully equips the agent to invoke the tool correctly: when it is allowed, what happens on failure, what side effects to expect, what response to expect, and how to avoid mistakes. Nothing essential is missing.

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 coverage is 0%, so the description must compensate. It does so by clarifying that model_hash refers to the hash of the FAILED model to delete, adding meaning beyond the bare string type. For a single simple parameter this is sufficient, though not deeply detailed.

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 ('permanently delete') and resource ('FAILED model'), clearly distinguishing it from read-only sibling tools like get_model and get_model_status. The capitalization and 'destructive and irreversible' warning make the operation unmistakable.

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?

It explicitly states the only valid use case: deleting models with status 'failed'. It also names the alternatives to check first (get_model, get_model_status) and explains that other statuses return a 409, so the agent knows when not to use the tool.

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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