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Glama

Pexels

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

A5/5.0
Behavior5/5

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

Annotations already declare read-only and idempotent, but the description adds valuable behavioral detail: data sources (SEC EDGAR/XBRL, FAERS), handling of off-calendar fiscal years, sorting by primary metric, and citation URIs. This goes well beyond the annotation hints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Despite being long, every sentence earns its place: trigger examples, preference guidance, data-source details, sorting behavior, and return type. It's front-loaded with usage examples and structured in a scannable way.

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?

With no output schema, the description explains return value (paired data + citation URIs), covers both entity types, mentions the lookup-count reduction (8–15 sequential lookups), and addresses edge cases (off-calendar fiscal years), making it complete for an agent.

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 both parameters with concrete examples (e.g., ['AAPL','MSFT'] for company, ['ozempic','mounjaro'] for drug), explains the enum values, and notes fiscal-year handling and data pulled per type, adding significant meaning 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 does side-by-side comparison of 2–5 companies or drugs in a single parallel call, with specific trigger phrases like 'X vs Y' and 'rank these companies'. It distinguishes from siblings by mentioning it replaces sequential lookups and covers entity types company/drug.

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?

Explicitly lists when to use (comparison queries, ranking, head-to-head) and provides a strong directive: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities'. This gives clear context and an alternative to avoid.

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
Disambiguation3/5

Many tools are clearly from distinct domains (Pexels media, Pipeworx data, Polymarket betting, utility), but within each domain there is significant overlap (e.g., ask_pipeworx vs ask_pipeworx_grounded vs deep_research, or numerous polymarket tools). Detailed descriptions help differentiate, but some tools could still be confused.

Naming Consistency3/5

Naming conventions vary: some tools use verb_noun (ask_pipeworx, compare_entities), others are single-word (photo, video), and some mix patterns (photo_curated vs photo_search). The Pexels subset is consistent, but the overall set includes tools from other ecosystems with different styles.

Tool Count2/5

With 38 tools, the set is bloated for a Pexels server. Only 8 tools are actually Pexels-related; the rest are Pipeworx data query, Polymarket betting, and utility tools. This mismatch makes the count inappropriate for the stated server purpose.

Completeness4/5

For the Pexels domain, the tool set covers search, fetch, and collections for both photos and videos, which is adequate. The unrelated tools add bloat but don't create gaps in the Pexels functionality. Minor missing features like uploads or editorial content are not critical.

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