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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. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already provide readOnly and idempotent hints. Description adds substantial behavioral context: pulls latest 10-K data from SEC EDGAR/XBRL, handles off-calendar fiscal years, sorts results by primary metric, returns paired data with pipeworx:// citation URIs, and replaces 8-15 sequential lookups. 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?

The description is longer than the minimum but every sentence adds value: trigger phrases, use guidance, type-specific details, sorting behavior, and output format. It is well-structured and front-loaded with usage intent, though slightly dense.

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?

For a 2-param tool with no output schema and strong annotations, the description covers input semantics, data sources, sorting, and return format (paired data + citation URIs). It also explains the efficiency advantage, making it sufficiently complete for correct invocation.

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 covers 100% of parameters with descriptions and examples. Description adds meaning by explaining type-specific data pulls (company: 10-K financials; drug: FAERS/FDA/trial counts) and clarifying values as tickers/CIKs or drug names beyond schema. This adds 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?

Clearly states side-by-side comparison of 2-5 companies or drugs, with specific verb 'compare' and resource types. Distinguishes from sequential single-pack lookups and says ALWAYS PREFER, making its purpose and advantage explicit.

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?

Provides explicit usage guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and lists trigger phrases ('X vs Y', 'which is bigger'). This clearly indicates when to use and implies when not to (for single entity lookups).

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

A3.9/5.0
Disambiguation2/5

The ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded trio are three variants of the same router — and the beta variant is explicitly stated to be identical to the stable one right now, making mis-selection nearly inevitable. The six polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) also have subtly overlapping boundaries where an agent could easily grab the wrong one.

Naming Consistency4/5

Nearly all tools use snake_case with a clear verb_noun or noun_compound shape (get_company_facts, resolve_entity, recent_changes, polymarket_edges). Minor deviations: the bare-verb memory trio (remember/recall/forget), the brand-style ask_pipeworx* naming, and a mix of verb-first vs. entity-first ordering, but the overall pattern is readable and predictable.

Tool Count3/5

34 tools is well above the typical well-scoped range, but the server's actual scope is enormous — a universal structured-data gateway, prediction-market suite, memory system, subscription system, and utility tools. However, the count feels inflated by genuine redundancy: ask_pipeworx_beta currently duplicates ask_pipeworx, and the polymarket cluster could plausibly be consolidated into fewer tools.

Completeness4/5

Each sub-domain has strong lifecycle coverage: entity resolution (resolve_entity, search_companies), company analysis (get_company_facts/filings, entity_profile, recent_changes, compare_entities), full CRUD for both memory and subscriptions, and an exhaustively covered prediction-market domain (research, edges, arb, fill risk, tracking, cross-venue). Minor gaps exist — there's no direct single-filing document fetch tool (search_within implies fetching via the gateway but no explicit getter), and the AI-visibility tools lack historical tracking — but these are workaround-able rather than blocking.

Resources