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usda-mcp-server

by io.github.cyanheads

List USDA Nutrients

usda_list_nutrients
Read-only

Look up the FDC nutrient reference table — all tracked nutrients with their numeric IDs, names, SR reference numbers, units, and categories. Use to resolve a nutrient name (e.g. "vitamin C") to its FDC ID (1162) before passing it to the nutrients[] filter on other tools. Filter by category (macronutrients, vitamins, minerals, lipids, amino_acids, or other) to narrow results. The data is static — call once and reuse the IDs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryNoFilter to a nutrient category. Omit to return all ~150 tracked nutrients. Options: macronutrients, vitamins, minerals, lipids, amino_acids, other.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
nutrientsNoNutrient reference entries matching the requested category, or all if category is omitted.

Schema Changelog

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

  1. Changed6 schema fields changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • addedInput schema / additionalProperties
      Added value: +false
    • changedOutput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • addedOutput schema / anyOf
      Added value: +[
+  {
+    "not": {
+      "required": [
+        "error"
+      ]
+    },
+    "required": [
+      "nutrients"
+    ]
+  },
+  {
+    "required": [
+      "error"
+    ]
+  }
+]
    • addedOutput schema / properties / error
      Added value: +{
+  "additionalProperties": {},
+  "description": "Present when the call failed. Absent on success.",
+  "properties": {
+    "code": {
+      "description": "JSON-RPC error code for this failure.",
+      "maximum": 9007199254740991,
+      "minimum": -9007199254740991,
+      "type": "integer"
+    },
+    "data": {
+      "additionalProperties": {},
+      "properties": {
+        "reason": {
+          "description": "Machine-readable failure mode.",
+          "type": "string"
+        },
+        "recovery": {
+          "additionalProperties": {},
+          "description": "Actionable next step for the caller.",
+          "properties": {
+            "hint": {
+              "type": "string"
+            }
+          },
+          "required": [
+            "hint"
+          ],
+          "type": "object"
+        },
+        "retryable": {
+          "description": "Whether retrying may succeed.",
+          "type": "boolean"
+        }
+      },
+      "type": "object"
+    },
+    "message": {
+      "description": "Human-readable description of what went wrong.",
+      "type": "string"
+    }
+  },
+  "required": [
+    "code",
+    "message"
+  ],
+  "type": "object"
+}
    • removedOutput schema / required
      Removed value: -[
-  "nutrients"
-]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations provide readOnlyHint=true and openWorldHint=false, so the description doesn't need to cover safety. It adds useful behavioral context: the data is static, so results can be cached and reused. This goes beyond the annotations and helps the agent plan calls efficiently.

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 sentences, each with a distinct purpose: what it does, how to use it, and a caching hint. The most important information (purpose and usage) is front-loaded, followed by a practical note. No filler or 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?

For a tool with only one optional parameter and an output schema, the description is complete. It covers the purpose, usage context, filtering options, and data characteristics. Nothing an agent needs to call it correctly is missing.

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 schema covers 100% of the parameters, with a clear enum and description for 'category'. The description repeats the enum options and adds 'narrow results', but doesn't add new meaning beyond the schema. Baseline of 3 applies since schema does the heavy lifting.

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 ('Look up') and resource ('the FDC nutrient reference table'), and specifies exactly what it returns (numeric IDs, names, SR reference numbers, units, categories). It distinguishes itself from the food-related siblings by focusing on nutrients, and even provides a concrete example ('vitamin C' → 1162).

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 explicitly says 'Use to resolve a nutrient name ... before passing it to the nutrients[] filter on other tools,' which gives clear when-to-use guidance. It also suggests filtering by category and notes the static nature ('call once and reuse'). It doesn't explicitly mention when not to use it, but the context with siblings makes the differentiation obvious.

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

A4.7/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: search for foods, retrieve single or batch nutrient profiles, compare foods side-by-side, and look up nutrient reference data. The descriptions explicitly cross-reference when to use which tool, eliminating ambiguity.

Naming Consistency5/5

All tool names follow a consistent 'usda_<verb>_<noun>' pattern, with verbs like search, get, get, list, compare. Plural/singular usage correctly reflects whether the tool handles one or many items, and the naming is predictable and readable.

Tool Count5/5

Five tools is well-scoped for a USDA food data server, covering search, retrieval (single and batch), comparison, and nutrient metadata lookup. Each tool earning its place with no redundancy or bloat.

Completeness5/5

The tool set fully covers the domain of querying USDA FoodData Central: discovery via search, detailed retrieval via get_food/get_foods, comparison via compare_foods, and reference data via list_nutrients. No obvious gaps exist for read-only access.

Resources