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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

A4.8/5.0
Behavior5/5

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

Annotations already mark it as read-only, idempotent, and non-destructive. The description adds valuable context: data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handling of off-calendar fiscal years, sorting by primary metric, and output format (paired data + citation URIs). No contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but front-loaded with example queries that aid quick understanding. Every sentence serves a purpose, though it could be slightly more streamlined.

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 (comparison, two entity types, multiple data points), the description covers all necessary aspects: when to use, parameter details, data sources, sorting, and output format. No output schema is needed because the description explains returns adequately.

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

Parameters4/5

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

Schema coverage is 100% with basic descriptions. The description adds meaning: for 'type' it clarifies the two options; for 'values' it specifies tickers/CIKs for companies and names for drugs, plus min/max constraints. It also explains sorting behavior. Good addition, though schema already covers basics.

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 explicitly states the tool performs side-by-side comparisons of 2-5 companies or drugs in one parallel call, listing example user queries. It distinguishes from sequential single-pack lookups, making the purpose clear and specific.

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 guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also explains what each entity type returns, helping agents decide when to use it.

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

C2.8/5.0
Disambiguation2/5

The set mixes two unrelated domains (Guild Wars 2 endpoints and a broad Pipeworx data-research suite), and within each there are near-duplicates: ask_pipeworx vs ask_pipeworx_beta are functionally identical, commerce_prices vs guild_wars_2_item_price vs commerce_listings overlap on Trading Post data, and ask_pipeworx/ask_pipeworx_grounded/deep_research all route questions to the same source catalog. An agent could easily select the wrong tool.

Naming Consistency3/5

Names are all snake_case and readable, but there is no consistent pattern: some are bare nouns (achievements, currencies, quaggans, worlds, build), some are verb_noun (resolve_entity, validate_claim, generate_llms_txt), some are ask_* (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded), and some are compound names (polymarket_kalshi_spread, guild_wars_2_item_price). Minor deviations would be fine, but this is a genuine mix of conventions.

Tool Count2/5

42 tools is far beyond the well-scoped range, and the majority are unrelated to the 'Guild Wars 2' server name (only ~11 tools are GW2 API endpoints; the rest are Pipeworx data-research/meta tools). This feels like two or three servers merged into one.

Completeness2/5

For a Guild Wars 2 server, the coverage is thin: it has items, prices, achievements, worlds, and WvW, but no recipes, guilds, characters, skills, maps, or PvE content. For the broader data-research domain implied by most tools, the surface is sprawling but still lacks depth in several areas. The result is a set that is neither complete for GW2 nor coherently scoped for anything else.