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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?

Beyond annotations (readOnlyHint=true, etc.), the description details data sources (SEC EDGAR, FAERS), handling of off-calendar fiscal years, result sorting by primary metric, and output 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 highly concise and well-structured: it opens with common triggers, states the core function, then details per type, and ends with output format. Every sentence adds value with no 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?

Given the tool's complexity, the description covers all necessary aspects: when to use, input format, data sources, handling of edge cases (e.g., fiscal years), and output structure (paired data + URIs). No gaps identified.

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?

Although schema coverage is 100%, the description adds significant value by explaining enum values ('company' vs 'drug') with specific data source details, and by providing examples for the array parameter (tickers/CIKs vs drug names).

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 tool performs side-by-side comparisons of 2-5 companies or drugs in one call, with specific data sources for each type. It distinguishes from siblings like entity_profile by emphasizing efficiency over sequential 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?

The description provides explicit natural language triggers (e.g., 'Compare X and Y') and strongly recommends this tool over sequential lookups when comparing entities. Input format examples further clarify usage.

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

B3.4/5.0
Disambiguation2/5

Many tools cluster around the same purpose: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route questions to similar data sources, and the polymarket_* family has several overlapping edge/arbitrage scanners. The four Recreation.gov tools are distinct but are buried among unrelated Pipeworx tools, making selection ambiguous.

Naming Consistency3/5

Names are all snake_case and readable, with recognizable prefix families like ask_pipeworx*, polymarket_*, and pipeworx_* plus verb_noun names like search_facilities and list_campsites. However, the conventions are mixed: bare verbs, brand prefixes, and composite names coexist, and nothing in the naming signals that this is a Recreation.gov server.

Tool Count1/5

This server is named Recreation Gov but only 4 of 35 tools relate to recreation facilities; the other 31 are a general-purpose data, research, and prediction-market platform. That is an extreme scope mismatch for the server's stated purpose.

Completeness2/5

For the Recreation.gov surface, basic search and detail retrieval exist, but key operations like campsite availability, reservations, and permits are missing. The dominant Pipeworx functionality is unrelated to Recreation.gov, so the tool set as a whole has no coherent domain coverage.