SLayer

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set_datasource_priority

Set datasource priority to resolve ambiguous model names. When a bare model name exists in multiple datasources, this order determines which datasource is chosen first.

Instructions

Configure how SLayer disambiguates bare model names that exist in multiple datasources.

When two datasources both define a model named users, calling edit_model("users") (no data_source=) is ambiguous. SLayer walks this priority list and picks the first datasource that has the requested name. If none of the candidates appear in the list, an AmbiguousModelError is raised.

Args: priority: Datasource names, most-preferred first. Each entry must already exist (run list_datasources first). Pass an empty list to clear the priority.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`priority`	Yes

Output Schema

TableJSON 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 provided, the description carries full behavioral disclosure and does so well: it explains the priority-list walk, first-match selection, AmbiguousModelError when no candidate matches, and empty-list clearing behavior.

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, then uses a concrete ambiguous-model example to make the behavior intuitive. Every part—purpose, example, resolution rules, parameter semantics—earns its place without redundancy.

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 simple one-parameter configuration tool, the description covers the scenario, prerequisites, error behavior, and clearing semantics. The output schema can handle return details, and no critical information 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?

The schema only specifies 'priority' as an array of strings, so the description is essential. It adds ordering semantics (most-preferred first), the requirement that names already exist, and the empty-list clearing behavior.

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: 'Configure how SLayer disambiguates bare model names.' The description clearly distinguishes this from sibling tools like get_datasource_priority and list_datasources by focusing on the configuration/set behavior.

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?

Provides clear context for when this tool is needed—when bare model names exist in multiple datasources—and gives an actionable prerequisite (run list_datasources first). It does not explicitly contrast with get_datasource_priority, but the read/write pairing is implied by the sibling names.

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