SLayer

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delete_model

Delete a semantic model by name. Optionally specify the data source to target the correct model when duplicates exist across datasources.

Instructions

Delete a semantic model.

Args: name: Model name to delete. data_source: Datasource the model belongs to. Required when the same name exists in multiple datasources (otherwise the priority list / single-match rules apply).

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": {
      "name": "<name>"
    },
    "name": "delete_model"
  }
}

Input Schema

Name	Required	Description	Default
`name`	Yes
`data_source`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.10.0Sep 3, 2026

TDQS

A3.7/5.0

Behavior2/5

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

No annotations are provided, so the description carries full responsibility for disclosing behavioral traits. It states that a semantic model is deleted, but it does not mention whether deletion is irreversible, whether it cascades to dependent objects, whether permissions are required, or what side effects may occur. This is a significant gap for a destructive 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 purpose is front-loaded in a single clear sentence, and the Args section adds only relevant parameter details. There is no filler or redundant wording; every line supports correct invocation.

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?

The description covers the tool's purpose and both parameters, and an output schema exists so return-value details need not be explained. However, for a destructive tool with no annotations, the absence of behavioral consequences and the vague priority-list reference leave the description incomplete for an agent that needs to invoke it safely in edge cases.

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 must compensate, and it does. It explains that `name` identifies the model to delete and that `data_source` serves as a disambiguator when the same name exists in multiple datasources. The reference to priority list / single-match rules adds meaning, though those rules are not fully defined.

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 and resource ('Delete a semantic model'), making it immediately clear what the tool does. It naturally distinguishes this from sibling tools like delete_datasource and create_model/edit_model, so there is no ambiguity about its core operation.

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

Usage Guidelines3/5

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

The description provides useful conditional guidance for when `data_source` is required, but it does not explicitly address when to use delete_model versus alternatives such as delete_datasource. The intended usage is implied by the tool name and first sentence rather than backed by clear selection rules.

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