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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"]).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already indicate read-only and non-destructive behavior. The description adds valuable behavioral context: it specifies data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), mentions handling of off-calendar fiscal years, and notes that results are sorted by primary metric. This goes beyond the 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?

The description is front-loaded with trigger phrases and key action. It is somewhat lengthy but each sentence adds value. Could tighten minor redundancies but overall well-structured for AI agent parsing.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, so the description partially covers return values (paired data + citation URIs). However, it lacks details on response structure, what happens if data is missing or an entity is not found, and any error handling. Given the tool's complexity, more completeness would be beneficial.

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 clear descriptions. The description adds meaning by explaining what each 'type' value retrieves (e.g., company gets latest 10-K revenue, net income, etc.; drug gets FAERS counts, trial counts) and gives examples for 'values'. This enhances the schema descriptions.

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 in a single call, using explicit trigger phrases like 'compare X and Y' and 'which is bigger'. It distinguishes itself from sibling tools like entity_profile by emphasizing parallel lookup 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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing strong usage guidance. It also lists example queries. However, it does not mention when NOT to use it (e.g., when needing non-financial/drug data) or alternatives like deep_research for more comprehensive analysis.

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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but the three ask_pipeworx variants (stable, beta, grounded) overlap significantly in functionality, as do the Polymarket tools (bet_research, arbitrage, edges, etc.), which could cause an agent to misselect without careful reading of descriptions.

Naming Consistency3/5

Naming follows multiple styles: verb_noun (ask_pipeworx, compare_entities), domain first (polymarket_arbitrage, bart_departures), and single words (remember, forget). There is no consistent pattern, making the set feel disjointed.

Tool Count3/5

35 tools is on the heavy side for a single server. While each tool has a defined role, the count suggests potential for consolidation (e.g., merging ask_pipeworx variants or Polymarket tools). The server's broad scope partially justifies the count.

Completeness4/5

The tool surface covers a wide range of functionalities: structured querying, entity lookup, comparison, fact-checking, subscription management, memory, and domain-specific tools for BART and Polymarket. Minor gaps exist (e.g., no direct API for some data sources), but overall it feels comprehensive.

Resources