mcp-mealie

parse_ingredients

Parse multiple ingredient strings from a JSON array, converting them into structured data for meal planning and recipe management.

Instructions

Parse many ingredient strings. ingredients_json is a JSON string array.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "ingredients_json": "<ingredients_json>"
    },
    "name": "parse_ingredients"
  }
}

Input Schema

Name	Required	Description	Default
`ingredients_json`	Yes
`parser`	No		nlp

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.7.0Jun 19, 2026

TDQS

C2.6/5.0

Behavior2/5

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

With no annotations, the description must fully disclose behavior, but it only states 'Parse' without any details on input validation, error handling, or side effects. The brevity leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely short (two sentences), which minimizes verbosity but at the cost of missing necessary details. It is not truly concise; it is under-specified.

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

Completeness2/5

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

Given the tool has two parameters, an output schema, and several sibling tools, the description is too sparse to provide complete context. Important aspects like return format (though output schema exists) and parameter constraints are not addressed.

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

Parameters2/5

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 explains 'ingredients_json' as a JSON string array, but the 'parser' parameter (with default 'nlp') is completely unexplained, missing potential options like 'nlp' or others.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool parses many ingredient strings, indicating a batch operation. However, it does not explicitly distinguish from sibling tools parse_ingredient (singular) and parse_recipe_ingredients, which could be confused.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like parse_ingredient or parse_recipe_ingredients. There is no context on prerequisites or exclusions.

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

Deploy Server