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Glama

Photon

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

A4.8/5.0
Behavior5/5

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

Annotations already indicate safe, read-only behavior. The description adds valuable details: correct handling of off-calendar fiscal years, results sorted by primary metric, and return of paired data with citation URIs. 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 somewhat long but efficient; every sentence adds value. It front-loads common query patterns. Minor redundancy could be trimmed, but overall well-structured.

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?

No output schema exists, but the description mentions return type (paired data + citation URIs). It explains what metrics are returned for each type. For a two-parameter tool, this is fairly complete, though more detail on response format would be beneficial.

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%, so baseline is 3. The description adds meaning: explains what each type pulls (financials for company, adverse events for drug) and specifies value formats (tickers/CIKs vs. drug names). This goes beyond the schema definitions.

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 comparison of 2-5 companies or drugs in one call, specifying data sources (SEC EDGAR, FAERS) and metrics. It distinguishes from siblings by noting it replaces 8-15 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?

The description lists explicit user triggers (e.g., 'Compare X and Y', 'which is bigger') and instructs to always prefer over sequential lookups. It provides clear context for when to use this tool over alternatives.

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

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, even within clusters like Pipeworx queries (ask_pipeworx vs ask_pipeworx_grounded vs deep_research) and Polymarket tools (bet_research, arbitrage, edges, etc.). Overlap is minimal and explicitly addressed in descriptions.

Naming Consistency5/5

All tool names use snake_case, and most follow a consistent verb_noun pattern (e.g., ask_pipeworx, compare_entities, resolve_entity). Exceptions like remember, reverse are still single words in the same style.

Tool Count4/5

32 tools is on the high side but justified by the wide scope: data querying, betting, memory, subscriptions, and utilities. Each tool earns its place, and the count is not excessive given the breadth.

Completeness4/5

The tool set covers the core workflows of querying structured data, comparing entities, validating claims, scanning dependencies, and monitoring, with few gaps (e.g., no direct data visualization). Minor gaps exist but are manageable.

Resources