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Glama

airports

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

A4.9/5.0
Behavior5/5

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

Goes well beyond annotations by disclosing data sources (SEC EDGAR/XBRL for companies, FAERS and FDA for drugs), handling of off-calendar fiscal years, sorting by primary metric, and inclusion of citation URIs. This is consistent with readOnlyHint, idempotentHint, and destructiveHint=false, with 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 dense and front-loaded with trigger phrases and a strong preference instruction. While lengthy, each sentence contributes unique value—usage examples, data sources, sorting behavior, and efficiency benefit. Slightly overlong but appropriately structured for a complex tool.

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?

For a tool with no output schema, the description thoroughly covers return format ('paired data + pipeworx:// citation URIs'), sorting behavior, data sources, and even handles edge cases like off-calendar fiscal years. It provides a complete picture for an AI agent to select and invoke the tool 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?

Although schema coverage is 100%, the description adds significant meaning: it explains exactly what each type retrieves (10-K revenue, net income, cash, debt for companies; adverse-event counts, approval counts, trial counts for drugs) and clarifies value formats (tickers/CIKs vs drug names). This enriches the bare 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's function: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It uses a specific verb ('compare') and resource ('entities'), and differentiates from sequential lookups and siblings like entity_profile by focusing on multi-entity comparison in a single call.

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 when-to-use guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It also lists trigger phrases ('X vs Y', 'which is bigger') and notes it replaces 8–15 lookups, effectively distinguishing from alternative approaches.

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

A3.5/5.0
Disambiguation2/5

Many tools have distinct purposes, but the three 'ask_pipeworx' variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are highly similar and likely cause confusion. Additionally, the toolset mixes airport-specific tools with unrelated financial and research tools, creating ambiguity about when to use which.

Naming Consistency2/5

Naming conventions are inconsistent: some tools use descriptive snake_case (ai_visibility_check, ask_pipeworx), others are single verbs (remember, forget, recall), and some include brand names (pipeworx_feedback). No clear pattern emerges across the 34 tools.

Tool Count1/5

34 tools is excessive for a server named 'airports'—only 3 tools directly relate to airports (search_airports, get_airport, calculate_distance). The majority are unrelated utilities (financial, prediction markets, memory), making the scope far too broad and unfocused.

Completeness1/5

The server severely lacks completeness for its stated airport domain: there are no tools for flights, airlines, runways, or real-time data. The other included domains (e.g., financial, prediction markets) are also incomplete, with e.g., only partial coverage of company data and no update/delete operations.

Resources