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Usda Fdc

by io.github.pipeworx-io

Search Foods

search_foods
Read-onlyIdempotent

Search USDA Food Data Central for food items by name, brand, or ingredient. Supports filtering by data type (Branded, Foundation, SR Legacy, Survey FNDDS, Experimental) and brand owner. Returns FDC id, description, data type, and publication date per result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesFree-text — name / brand / ingredient
sort_byNodataType.keyword | lowercaseDescription.keyword | fdcId | publishedDate (default relevance)
data_typeNoFilter to data type(s). Comma-sep. Options: Branded | Foundation | Survey (FNDDS) | SR Legacy | Experimental
page_sizeNo1-200 (default 50)
sort_orderNoasc | desc
brand_ownerNoRestrict to a brand owner
page_numberNo1-based page (default 1)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
foodsNoArray of food items matching search
totalHitsNoTotal number of matching foods
currentPageNoCurrent page number
foodSearchCriteriaNoSearch criteria used

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds that results include FDC id, description, data type, and publication date. It does not mention pagination behavior or rate limits, but the schema covers page_size and page_number. No contradiction with 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 two sentences long, front-loads the action and resource, and includes key filtering options and return fields. Every word adds value with no filler.

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 the tool has 7 parameters and an output schema, the description covers the core search capability, filtering, and return fields. It does not detail sorting or pagination, but the schema provides that. Overall, it is sufficiently complete for an agent to understand the 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 description coverage is 100%, so the schema already documents all 7 parameters with descriptions. The description only restates that filtering by data type and brand owner is supported, adding no new semantic value beyond the schema. Baseline 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 the specific verb 'search' and identifies the resource as 'USDA Food Data Central'. It clearly states the tool searches for food items by name, brand, or ingredient, with filtering capabilities. This distinguishes it from siblings like 'list_foods' and 'get_food'.

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

Usage Guidelines3/5

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

The description mentions filtering by data type and brand owner, which implies usage context but does not explicitly state when to use this tool over alternatives like 'list_foods' (which likely retrieves all) or 'get_food' (single item). No when-not or exclusion criteria are provided.

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

B3.3/5.0
Disambiguation2/5

The tool set mixes USDA food data tools with a large number of unrelated tools (Polymarket betting, AI visibility, npm scanning, etc.), causing significant overlap in purpose. Many tools like ask_pipeworx, ask_pipeworx_grounded, deep_research, and bet_research all perform research/lookups with similar scopes, making it difficult for an agent to select the appropriate tool.

Naming Consistency3/5

Tool names generally follow a descriptive verb_noun pattern (e.g., list_foods, search_foods), but there is inconsistency in prefixes (ask_pipeworx vs. pipeworx_feedback vs. polymarket_arbitrage) and some names are long and varied. The naming is readable but not highly predictable.

Tool Count2/5

35 tools is excessive for a server ostensibly focused on USDA Food Data Central. Many tools are unrelated to food (e.g., Polymarket, Kalshi, npm scanning, subscription management), making the server feel bloated and unfocused. A typical food data server would have 5-10 tools.

Completeness4/5

For the USDA FDC domain, the tool surface is complete: it includes list, search, get, and nutrient retrieval. However, the presence of many unrelated tools dilutes the server's focus. The food-specific operations are well-covered, but the overall server lacks coherence.

Resources