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get_conventions

Get the current locations, categories, quantity units, and house rules for your Grocy instance so you can reference correct IDs without guessing, even after renames.

Instructions

This instance's locations (with ids), categories, quantity units, and whatever house rules are configured — the reference every other tool's docstring points at.

Call this once at the start of a session rather than guessing or trial-and-erroring an id. Locations, categories and units are read live, so this stays right even after something is renamed in the web UI.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden, and it delivers meaningful context: locations, categories, and units are 'read live' and stay correct after web UI renames. It implies a safe read-only query, although it never explicitly states that no mutation occurs or discusses any output-size or freshness caveats beyond live reading.

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?

Three tight sentences front-load the core content, then give a usage instruction and a freshness guarantee. No filler or repetition beyond a natural recapitulation of the entities named.

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 parameterless metadata-retrieval tool with an output schema and no annotations, this description covers what the agent needs: what is returned, why it is the canonical source, when to call it, and how fresh the data is. 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?

The tool takes zero parameters and the schema coverage is 100%, so there are no parameter semantics to flesh out. The description adds useful context about ids being included in the returned locations, which agents need for later calls.

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 exactly what the tool exposes — locations with ids, categories, quantity units, and house rules — and explicitly identifies it as the cross-referenced reference for other tools. This makes the tool's role unambiguous and distinct from the many operation-oriented siblings.

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 gives an explicit directive: 'Call this once at the start of a session rather than guessing or trial-and-erroring an id.' This tells the agent exactly when and why to use it, preempting the common failure mode of guessing identifiers.

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