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recipes

by io.github.pipeworx-io

Meals By Ingredient

meals_by_ingredient
Read-onlyIdempotent

Find all recipes using a specific ingredient (e.g., "chicken", "garlic", "pasta"). Returns meal names and IDs to pass to get_meal.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ingredientYesIngredient name to filter by

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
mealsYesList of meals containing the ingredient
totalYesNumber of meals containing the ingredient
ingredientYesThe ingredient that was searched

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "ingredient": {
+      "description": "The ingredient that was searched",
+      "type": "string"
+    },
+    "meals": {
+      "description": "List of meals containing the ingredient",
+      "items": {
+        "properties": {
+          "id": {
+            "description": "TheMealDB meal ID",
+            "type": "string"
+          },
+          "name": {
+            "description": "Meal name",
+            "type": "string"
+          },
+          "thumbnail_url": {
+            "description": "URL to meal thumbnail image",
+            "type": [
+              "string",
+              "null"
+            ]
+          }
+        },
+        "required": [
+          "id",
+          "name",
+          "thumbnail_url"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "total": {
+      "description": "Number of meals containing the ingredient",
+      "type": "integer"
+    }
+  },
+  "required": [
+    "ingredient",
+    "total",
+    "meals"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "ingredient": "chicken"
+  },
+  {
+    "ingredient": "garlic"
+  }
+]
  3. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, covering the safety profile. The description adds behavioral context by stating it returns 'all recipes' and specifies the output format (meal names and IDs), which is useful beyond the annotations.

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 concise, consisting of two sentences that immediately state the action and key details. It is well-structured and free of unnecessary content.

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?

The tool is simple (one parameter), has comprehensive annotations, and includes an output schema. The description fully covers the tool's purpose, examples, and downstream usage, so nothing is missing for this low-complexity 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?

The input schema already fully describes the only parameter with 100% coverage. The description adds examples but no additional semantic depth beyond what the schema provides, so the baseline of 3 is appropriate.

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 uses a specific verb 'Find' with a clear resource and scope: 'all recipes using a specific ingredient'. It also provides examples and mentions returning meal names and IDs, which distinguishes it from siblings like random_meal (random selection) and search_meals (likely keyword search).

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?

The description clearly implies when to use this tool (when filtering recipes by ingredient) and provides a workflow hint by stating the results are 'to pass to get_meal'. However, it does not explicitly exclude alternatives or name sibling tools for different use cases.

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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TDQS

A4/5.0
Disambiguation5/5

Each tool has a highly specific purpose with detailed descriptions, making them easily distinguishable. Overlaps are minimal; for instance, Pipeworx and Polymarket tools have distinct roles within their domains.

Naming Consistency4/5

Most tools follow a consistent snake_case pattern (e.g., ask_pipeworx, compare_entities), but a few single-word names (e.g., forget, recall) deviate slightly, causing minor inconsistency.

Tool Count2/5

The server is named 'recipes' but contains only 4 recipe-related tools out of 34. The majority cover unrelated domains like finance, betting, and data queries, making the scope overly broad and misaligned with the server name.

Completeness2/5

For the 'recipes' domain, essential CRUD operations and features like meal planning are missing. While the general tool set is extensive, it lacks critical recipe-related functionality, leaving significant gaps for the intended purpose.

Resources