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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

A4.8/5.0
Behavior5/5

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

Annotations already declare read-only, open-world, idempotent, and non-destructive. The description adds substantial behavioral context: data sources (SEC EDGAR/XBRL, FAERS), handling of off-calendar fiscal years, sorting by primary metric, and return of paired data with citation URIs. No contradiction.

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 dense and well-structured, leading with example queries, then core functionality, type-specific details, sorting behavior, and output. Some redundancy in the query examples could be trimmed, but each sentence contributes useful information.

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?

Given the tool's complexity and lack of output schema, the description is remarkably complete: it states return format (paired data + citation URIs), sorting order, data specifics, and handles nuanced cases like off-calendar fiscal years. All key aspects are covered.

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?

The schema covers both parameters at 100%, but the description enriches the 'type' parameter by explaining that 'company' pulls 10-K financials and 'drug' pulls FAERS/FDA/trial data. This goes beyond the schema's minimal enum description, though the values array is adequately specified in 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 explicitly states the tool performs side-by-side comparison of 2–5 companies or drugs in one parallel call, using specific verbs like 'compare' and 'rank.' It distinguishes itself from sequential single-pack lookups, making its purpose unambiguous and distinct from sibling tools.

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 guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also gives concrete query examples and elaborates on what each 'type' retrieves, giving clear when-to-use and how-to-use instructions.

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

A3.6/5.0
Disambiguation1/5

Multiple tools have nearly identical purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all handle natural-language data queries, with ask_pipeworx_beta explicitly duplicating ask_pipeworx. The prediction-market cluster (polymarket_edges, polymarket_arbitrage, bet_research) also heavily overlaps, and ai_visibility_check is a single-entity version of scan_competitor_ai_presence. An agent would frequently be unable to tell which tool to select.

Naming Consistency2/5

All names are snake_case, but the pattern is inconsistent: some are verb_noun (generate_llms_txt, resolve_entity), some are bare verbs (forget, recall, subscribe), and several are noun-first domain names (polymarket_edges, pipeworx_trending, entity_profile). There is no uniform verb convention, and the mix makes it hard to predict what a tool does from its name.

Tool Count2/5

At 33 tools, this is well above the 'heavy' threshold and includes several near-duplicates: three ask_pipeworx variants and six polymarket_* tools. While the underlying platform is broad, this meta-layer could be consolidated to 15-20 tools without losing capability.

Completeness4/5

The core data-query workflow is well covered: ask, deep research, entity profile, compare, validate, resolve ID, and search inside documents. The memory lifecycle (remember/recall/forget) and subscription lifecycle (subscribe/list/recent_alerts/unsubscribe) are also complete. However, the set includes unrelated utilities (generate_paragraphs, scan_dependency, generate_llms_txt) that don't belong to the main data domain, and there is no direct tool to execute a raw discovered tool by name.

Resources