Cronometer MCP

list_custom_foods

Read-onlyIdempotent

List every custom food and recipe you own, separate from database results, to audit your library or find foods with broken names. Leave the query empty to review everything, then fix entries individually.

Instructions

Every custom food and recipe you own, with no database results mixed in.

Leave query empty for the whole library. This is the way to audit it: to find, say, every food whose name lost its Finnish characters, list them all and read the names, then fix each with update_custom_food.

Retired foods are hidden by Cronometer's own library listing, so include_retired cannot bring them back; it only affects rows already returned.

Args: query: Narrow the list by name. Empty lists everything. include_retired: Keep retired foods in the result when they appear.

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": "list_custom_foods"
  }
}

Input Schema

Name	Required	Description	Default
`query`	No
`include_retired`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.1.0Nov 14, 2026

TDQS

A4.8/5.0

Behavior5/5

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

The description meaningfully discloses behavior beyond the annotations: include_retired cannot surface retired foods hidden by Cronometer's own listing and only affects rows already returned. This is a subtle, non-obvious behavioral trait an agent needs to avoid misuse.

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 a concrete usage example and a caveat about retired foods. The Args section is cleanly separated. Every sentence adds useful information 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?

With only two optional parameters, clear parameter semantics, and an existing output schema, the description covers all essential behavior for correct invocation. The retired-food caveat and audit-oriented guidance make it complete for practical use.

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 coverage is 0%, but the Args section fully compensates by explaining query narrows by name, empty query lists everything, and include_retired only retains already-returned retired rows. This adds real semantics beyond the bare type/default declarations in the 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?

The description clearly explains the tool lists 'every custom food and recipe you own' and differentiates it from database search results with 'no database results mixed in.' This is a specific verb+resource pairing that distinguishes it from search_foods and other library tools.

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?

It gives concrete usage guidance: leave the query empty for the full library and use this tool to audit custom foods, including a practical example with updating names afterward. It implies when not to use it (when you need database results), but doesn't explicitly name alternative tools.

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

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