SLayer

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validate_models

Check stored semantic models against live database schemas and list pending column, measure, join, filter, or model drops to remove. Read-only validation returns JSON without modifying storage.

Instructions

Diff persisted SLayer models against the live database schema(s).

Returns a JSON-serialized list of pending delete operations (column drops, measure drops, join drops, filter removals, whole models) needed to keep stored models valid against the current live state. Read-only — does not modify storage.

Args: data_source: Datasource name to validate. When omitted, every datasource is validated concurrently and results are concatenated.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "validate_models"
  }
}

Input Schema

Name	Required	Description	Default
`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

A4.8/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and does so well: it explicitly states 'Read-only — does not modify storage', describes what the returned list contains, and discloses the behavior when data_source is omitted (validates all datasources concurrently and concatenates results).

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 front-loaded with the core purpose, followed by return behavior, safety note, and parameter semantics. Every sentence earns its place; no filler or repetition.

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 tool with one optional parameter and an output schema, the description is complete: it explains the input, the output shape, the no-modification guarantee, and the default all-datasources behavior. Nothing needed to call it correctly is missing.

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

Parameters5/5

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. It fully explains the single parameter: 'Datasource name to validate', including the omission behavior and result concatenation. This adds meaning well beyond the raw 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: 'Diff persisted SLayer models against the live database schema(s)'. It also clarifies the concrete output—a JSON list of pending delete operations—which distinguishes it from siblings like query, inspect_model, and delete_model.

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?

The description gives clear context for when to use the tool: to validate persisted models against live schemas and preview required deletions. It does not explicitly name alternative tools or exclusions, but the purpose is specific enough that an agent can select it appropriately among the sibling tools.

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