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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

A4.9/5.0
Behavior5/5

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

Discloses data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handles off-calendar fiscal years, sorting by primary metric, and return format (paired data + citation URIs). Complements annotations with specific behavioral details.

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?

Somewhat verbose but well-structured, front-loaded with trigger phrases. Every sentence adds value, though minor redundancy could be trimmed.

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?

Covers all necessary aspects: purpose, when to use, parameter details, data sources, sorting, and output format. No output schema, but description sufficiently explains return structure.

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 description adds extra meaning: example values for 'values', explanation of what each 'type' does, and constraints (max 5, min 2). Enhances understanding beyond schema.

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, with specific trigger phrases. It distinguishes itself from siblings by stating it replaces sequential single-pack 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?

Explicitly says when to use (comparing entities, ranking) and when not to ('ALWAYS PREFER over sequential single-pack lookups'). Provides clear guidance on type and values parameters.

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

A4.1/5.0
Disambiguation3/5

Most tools have distinct purposes, but there is notable overlap among ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research—all of which route questions to the same underlying catalog. The polymarket_* family also has several opportunity-scanning tools (edges, arbitrage, bet_research) that agents could confuse without reading the long descriptions carefully.

Naming Consistency5/5

All 34 tool names use lowercase snake_case with a clear verb-first or noun-descriptive pattern (list_feeds, read_feed, remember, resolve_entity, polymarket_arbitrage). Even compound names like ai_visibility_check and ask_pipeworx_grounded follow a predictable, consistent style. No mixed conventions or camelCase deviations.

Tool Count2/5

34 tools is far more than the apparent 'Gaming Feeds' scope suggests—only list_feeds, read_feed, and fetch_feed actually relate to gaming feeds. The rest form a sprawling data-research and prediction-market suite, creating a severe mismatch between the server name and its actual tool surface.

Completeness4/5

Viewed as a general Pipeworx data-access platform, the tool set is quite complete: question routing, grounded answers, entity resolution, profiles, comparisons, claim validation, memory, subscriptions, alerts, feed reading, and tool discovery are all covered. The only notable gaps are feed management (no create/update/delete for custom feeds) and a few odd add-ons like generate_llms_txt that feel outside the core domain.