usda-mcp-server

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

Name	Required	Description	Default
`category`	No	Filter to a nutrient category. Omit to return all ~150 tracked nutrients. Options: macronutrients, vitamins, minerals, lipids, amino_acids, other.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`error`	No	Present when the call failed. Absent on success.
`nutrients`	No	Nutrient reference entries matching the requested category, or all if category is omitted.

Schema Changelog

Changes observed during successful MCP inspections.

Changed6 schema fields changedAug 25, 2026

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"
-]
```

First observedAug 13, 2026

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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