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Law Feeds

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 the tool as readOnly, idempotent, openWorld, and non-destructive. The description adds useful behavioral context: data sourcing from SEC EDGAR/XBRL for companies (handling off-calendar fiscal years), FAERS data for drugs, and results sorted by primary metric. 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.

Conciseness4/5

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

Description is moderately lengthy but well-structured: starts with example triggers, then explains behavior for each type, mentions sorting, and quantifies value (replaces 8–15 lookups). Every sentence adds information; could be slightly trimmed but remains clear.

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 covers input requirements, data sources for both types, sorting behavior, and output format (paired data + citation URIs). For a comparison tool with moderate complexity, this is comprehensive.

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%, providing baseline 3. The description adds additional clarity: for 'values' parameter, it specifies tickers/CIKs for companies and drug names, and explains what data type='company' vs 'drug' returns, enhancing meaning 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?

The description clearly states the tool's purpose: side-by-side comparison of 2–5 companies or drugs in one parallel call. It lists trigger phrases, specifies entity types, and distinguishes from likely siblings like 'entity_profile' by noting it replaces 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?

Explicitly advises 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' Provides typical user utterances such as 'Compare X and Y' or 'which is bigger,' making it easy for the AI to recognize when to invoke this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Law Feeds toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Multiple tool families overlap heavily: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded all route through the same 5,721 tools, polymarket_edges/polymarket_arbitrage/bet_research/polymarket_fill_risk all target prediction-market opportunities, and ai_visibility_check vs scan_competitor_ai_presence cover the same probe. An agent would struggle to pick the right one without reading every description carefully.

Naming Consistency3/5

There are coherent subfamilies (ask_pipeworx_*, polymarket_*, remember/recall/forget, list/read/fetch_feed), but the overall set mixes verb-first names (list_feeds, validate_claim, fetch_feed) with noun-first names (entity_profile, bet_research, deep_research) and adjective-led names (recent_alerts, recent_changes). The inconsistency is noticeable but not chaotic.

Tool Count2/5

34 tools is heavy for a server branded 'Law Feeds,' and many tools are off-domain (Polymarket betting, npm dependency scanning, AI visibility marketing, generic memory). The breadth could justify a larger catalog, but the overlapping research/Polymarket tools inflate the count beyond what the surface needs.

Completeness3/5

As a general data-gateway, the set is fairly complete: routing, grounded answers, deep research, entity resolution, comparison, subscriptions, memory, and feedback are all covered. Relative to the 'Law Feeds' identity, though, the surface is shallow — only list_feeds, read_feed, and fetch_feed serve that purpose, with no feed search, management, or update capabilities.

Resources