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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

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld, non-destructive. Description adds that it handles off-calendar fiscal years, sorts results by primary metric, and returns paired data with citation URIs. No contradictions, but could mention edge cases like missing data.

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?

Description is dense and front-loaded with trigger phrases and purpose. While slightly verbose, every sentence adds value. Structured with em dashes for readability.

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 complexity of two entity types and multiple data sources, the description covers purpose, data sources, result sorting, and citation URIs. No output schema, but description hints at response format. Could be more specific about 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?

Input schema has 100% coverage with descriptions for both parameters. Description goes further by explaining the type enum's data sources and providing examples for values (tickers/CIKs vs drug names), adding significant value beyond the 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?

Description explicitly states the tool performs side-by-side comparison of 2–5 companies or drugs, lists trigger phrases, and distinguishes it from sequential lookups. It clearly specifies data sources for each type.

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 'ALWAYS PREFER over sequential single-pack lookups' and explains that it replaces 8–15 sequential lookups. Provides clear context for when to use this tool vs alternatives like entity_profile.

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

Several tools overlap in purpose (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim all handle factual queries), which could confuse an agent. However, detailed descriptions help differentiate them, so the confusion is moderate.

Naming Consistency4/5

Most tools follow a consistent snake_case verb_noun pattern (e.g., resolve_entity, compare_entities). A few irregular verbs (forget, recall, remember) and diverse prefixes (pipeworx_, polymarket_) lower consistency slightly.

Tool Count3/5

33 tools is high but each serves a distinct purpose within a broad domain (data querying, prediction markets, security, memory, etc.). The number feels slightly excessive for a single server, but not extreme.

Completeness4/5

The tool surface covers many aspects of data retrieval, prediction market analysis, and security checks. Minor gaps exist (e.g., no dedicated WHOIS or CVE lookup), but core workflows are well-supported.

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