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Emailable

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 indicate read-only, idempotent, not destructive. The description adds specific behavioral details: data sources (SEC EDGAR/XBRL for companies, FAERS/FDA/clinical trials for drugs), handling of off-calendar fiscal years, sorting by primary metric, and citation URIs. No contradictions.

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?

Highly concise yet information-rich. Front-loaded with example queries, followed by clear explanation of behavior for each type, and a concluding sentence on efficiency. Every sentence adds value.

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?

Despite no output schema, the description explains what is returned (paired data with citation URIs) and sorting order. Both entity types are fully covered. For a tool with 2 parameters and simple behavior, the description is complete.

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 meaning: explains type values ('company' vs 'drug') and the expected value formats (tickers/CIKs for companies, drug names for drugs). It also clarifies sorting behavior and data sources.

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 does side-by-side comparison of 2-5 companies or drugs, with a specific verb-resource ('compare entities') and explicit example queries. It distinguishes from siblings like entity_profile by emphasizing parallel 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?

Explicit guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' with example commands. It explains what types ('company' or 'drug') and constraints (2-5 items) are appropriate, leaving no ambiguity about when to use this tool.

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

A4.4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but the ask_pipeworx family (standard, beta, grounded) and validate_claim vs ask_pipeworx_grounded may cause some confusion for agents despite detailed descriptions.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern, and most are verb_noun structures (e.g., ask_pipeworx, compare_entities, resolve_entity), making them predictable and easy to distinguish.

Tool Count4/5

With 32 tools, the set is larger than ideal but well-justified by the broad scope of data sources and functionalities (email verification, SEC/FDA lookups, prediction market analysis, memory, subscriptions).

Completeness5/5

The tool set covers the full pipeline from data discovery (discover_tools, suggest_questions) to retrieval, analysis, comparison, verification, and monitoring, with no obvious gaps for its intended use cases.

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