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Glama

Singstat Sg

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

A4.9/5.0
Behavior5/5

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

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds detailed behavioral info: specific data sources (SEC EDGAR/XBRL for companies, FAERS/FDA/clinical trials for drugs), off-calendar fiscal year handling, and sorting by primary metric. 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 dense but well-front-loaded with key phrases and examples. Every sentence adds value, though a minor redundancy exists (e.g., repeating 'type="company" pulls...'). Still, it earns its length.

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?

Despite no output schema, the description mentions return format ('paired data + pipeworx:// citation URIs'). It covers both entity types, data sources, and edge cases (off-calendar fiscal years). Adequate for an agent to select and invoke correctly.

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%, but the description enriches parameter meaning: 'type' enum values are explained with what data they pull (revenue/net income/cash/debt for company; adverse events/approvals/trials for drug), and the 'values' parameter format is clarified (tickers for company, names for drug).

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 comparisons of 2–5 companies or drugs in a single parallel call, using clear verbs like 'compare' and specific resources ('companies or drugs'). It distinguishes itself from sibling tools like entity_profile by focusing on multi-entity comparison.

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 instructs 'ALWAYS PREFER over sequential single-pack lookups when comparing entities,' providing strong usage guidance. It also includes example queries ('which is bigger...') and implies when not to use (avoid 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

A4.1/5.0
Disambiguation5/5

Each tool has a clearly defined purpose with detailed descriptions, and despite some composite tools, there is no ambiguity in when to use which one.

Naming Consistency3/5

Tool names mix verb_noun and noun_noun patterns, with some purely verb names, lacking a consistent convention. While readable, the pattern is not predictable.

Tool Count3/5

With 23 tools, the server is on the heavy side for a mixed-purpose toolset. Each tool earns its place, but the count feels slightly bloated for the breadth of domains covered.

Completeness3/5

The server covers several domains (AI visibility, betting, entity lookup, etc.) with reasonable depth, but the lack of a unified domain means some areas feel under-served (e.g., no update/delete except memory).

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