Skip to main content
Glama
Sign In
mcp-mealie

mcp-mealie

by obrien-matthew
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

parse_ingredient

Convert an ingredient string into structured quantity, unit, and food components. Choose between NLP or regex parser.

Instructions

Parse a single ingredient string into structured quantity/unit/food.

parser is "nlp" (default, CRF model) or "brute" (regex fallback).

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": {
      "text": "<text>"
    },
    "name": "parse_ingredient"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes
parserNonlp

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.0

TDQS

A4.2/5.0
Behavior4/5

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

No annotations provided, but the description discloses the tool's behavior: takes a text string, returns structured data, and the parser parameter affects parsing method. It is a read-only parsing tool with no side effects.

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 are front-loaded with purpose and parameter explanation. No unnecessary words.

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?

Given no output schema, the description lacks return format details. But with only two simple parameters, it is nearly complete for a single-string parsing tool.

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

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description adds value by explaining 'text' as the ingredient string and 'parser' as 'nlp' or 'brute'. However, it does not describe expected format of 'text' or the return structure.

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 states the verb 'parse', the resource 'a single ingredient string', and the output 'structured quantity/unit/food'. This distinguishes it from siblings like 'parse_ingredients' (plural) and 'parse_recipe_ingredients'.

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 explains the 'parser' parameter options ('nlp' default, 'brute' regex fallback), giving context for which to use. However, no explicit when-not-to-use or alternatives compared to sibling tools.

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

Deploy Server

Other Tools