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Usda Fdc

by io.github.pipeworx-io

Compare Entities

compare_entities
Read-onlyIdempotent

"Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valuesYesFor company: 2–5 tickers/CIKs (e.g., ["AAPL","MSFT"]). For drug: 2–5 names (e.g., ["ozempic","mounjaro"]).

TDQS

A5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds significant behavioral context: data sources (SEC EDGAR, FAERS), handling of fiscal year differences, metrics pulled, sorting, and return format with citation URIs. No contradictions 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 a single well-structured paragraph with front-loaded key information. Every sentence adds value, covering usage patterns, data sources, sorting, and efficiency. No redundant phrasing.

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?

Given the tool's complexity, the description covers essential aspects: usage, input parameters, data sources, sorting, and output hints. The annotations provide additional safety context. No gaps that would hinder correct invocation.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds substantial meaning beyond the schema. It explains the 'type' parameter's specific data sources and the 'values' parameter with concrete examples and constraints. This helps the agent understand the expected input.

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 starts with explicit verb phrases like 'Compare X and Y' and 'side-by-side comparison', clearly stating the tool's purpose. It differentiates from sibling tools like entity_profile by explicitly stating preference over sequential single-entity lookups.

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

Usage Guidelines5/5

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

Provides explicit query patterns, specifies the tool should be used for comparing 2-5 companies or drugs, and instructs to prefer it over sequential single-pack lookups. The description gives clear context on when to use.

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.

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