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Glama

Semanticscholar

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

A4.7/5.0
Behavior5/5

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

Even with annotations already declaring readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, the description adds significant behavioral context: it reveals data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), handles special cases (off-calendar fiscal years), and describes output format (paired data + citation URIs). No contradictions.

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 two paragraphs, relatively concise given the breadth of information. It front-loads example queries and key instructions. Every sentence adds value, though some details could be streamlined or better organized (e.g., data specifics integrated more tightly).

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 absence of an output schema, the description adequately covers return format (paired data + citation URIs). It addresses behavioral aspects, data sources, and special cases. The description is complete for a tool of this complexity, enabling correct invocation without further clarification.

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% with two parameters (type and values). The description adds crucial semantics: explains enum values ('company' pulls 10-K data, 'drug' pulls FAERS data), specifies input format (tickers/CIKs vs drug names), and constrains array length (2–5). This greatly enhances understanding 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 performs side-by-side comparison of 2–5 companies or drugs. It provides multiple example queries that trigger the tool, and explicitly distinguishes it from sequential single-entity lookups and other sibling tools like 'entity_profile'.

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 instructs to 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing clear context for when to use this tool. It also notes results are sorted by primary metric for interpreting terms like 'largest.' However, it doesn't explicitly mention when not to use it or alternative tools beyond sequential 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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, especially the core ones like get_paper, search_papers, and entity_profile. However, some pairs like ask_pipeworx and ask_pipeworx_grounded, or the polymarket tools, could cause momentary confusion, though descriptions help differentiate.

Naming Consistency2/5

Naming patterns are inconsistent: tools use verb_noun (e.g., get_paper), noun_phrase (e.g., polymarket_arbitrage), and bare verbs (e.g., forget, recall). There is no unifying pattern, and styles like 'pipeworx_feedback' vs 'search_papers' further add to the inconsistency.

Tool Count3/5

With 34 tools, the set covers a broad range of domains (academic papers, company data, prediction markets, memory, subscriptions). While the scope justifies the number, it feels slightly heavy and could benefit from consolidation or clearer grouping.

Completeness4/5

The tool set covers major functionalities for research, data retrieval, and monitoring, with only minor gaps (e.g., limited to US public companies, npm-only dependency scanning). Overall, the surface is comprehensive for the stated capabilities.

Resources