TimeLib MCP

get_store_context

Retrieve store defaults for regions, currencies, sales channels, and stock locations before creating products, eliminating guesswork for currency_code, sales_channel_id, and location_id.

Instructions

Return store defaults an agent needs BEFORE creating products: regions & their currency codes, sales channels, stock locations, and price preferences. Call this first when unsure about currency_code / sales_channel_id / location_id.

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": "get_store_context"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 4, 2026

TDQS

A4.4/5.0

Behavior3/5

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

There are no annotations provided, so the description carries full burden for behavioral disclosure. It describes a read/context operation implicitly ('Return store defaults'), which strongly implies non-destructive behavior. However, it doesn't explicitly state it performs no side effects, requires no auth, or describe the return format. Fair but not rich.

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?

Two sentences, zero wasted words. First sentence delivers the purpose and the data scope; second sentence gives the actionable 'when-to-use' trigger. Exceptionally tight and front-loaded.

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

Completeness4/5

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

For a zero-parameter, context-priming tool with no annotations and no output schema, it describes what it returns conceptually and when to call it. The main gap is not describing the exact return structure (e.g., how to extract each code/ID), but given its simplicity the description is adequate and complete enough to guide correct invocation.

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?

The tool has 0 parameters and 100% schema coverage (nothing to cover), but the description compensates by specifying exactly what data the agent receives (regions, currency codes, sales channels, stock locations, price preferences). Since 0 params = baseline 4 by the rubric, it earns the comfortable baseline.

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?

Specific verb+resource: 'Return store defaults an agent needs BEFORE creating products.' It lists the specific data delivered (regions & currency codes, sales channels, stock locations, price preferences), distinguishing this from sibling product creation/listing tools clearly. It's a distinct setup/context-gathering tool.

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?

Explicitly says 'Call this first when unsure about currency_code / sales_channel_id / location_id.' This directly ties usage to a precise trigger condition (uncertainty about specific identifiers) and positions it as a precursor to create_product. Strong exclusions/context guidance.

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