Skip to main content
Glama

Shopify

by io.github.pipeworx-io

Scan Dependency

scan_dependency
Read-onlyIdempotent

Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
packageYesnpm package name. Scoped packages (e.g. "@types/node") are accepted.
versionNoSpecific version to check (e.g., "18.3.1"). Defaults to the latest published version when omitted.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare the tool safe and idempotent; the description adds critical behavioral details: composite fan-out, partial failure with graceful degradation, 5-30s delay for first bundlephobia measurement, and sources_failed listing. No contradiction 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 a single dense paragraph with a clear, front-loaded purpose. Every sentence earns its place, though minor restructuring could improve readability. Approximately 150 words with no waste.

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 fully enumerates return fields, failure behavior, and constraints. It covers the tool's complexity adequately, ensuring an agent can confidently invoke and interpret results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% coverage with descriptions. The description adds value by clarifying scoped package acceptance and default version behavior. While schema already covers well, the description provides practical nuance.

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 purpose as a composite check for evaluating npm packages, listing specific data sources (deps.dev, bundlephobia) and the return summary block. It distinguishes from siblings by noting NPM-only scope and directing other ecosystems to a different tool.

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 states when to use: when agent asks about safety, popularity, size, or cost of adding an npm package. Also provides exclusion criteria: non-NPM ecosystems go to deps.dev:version directly. Partial failure behavior is explained.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Shopify toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.3/5.0
Disambiguation2/5

Many tools overlap in purpose, especially the Pipeworx data retrieval tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and the Polymarket betting tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.). The Shopify-specific tools are distinct, but the overall set is a confusing mix of domains, making it hard for an agent to select the right tool.

Naming Consistency2/5

Naming conventions are inconsistent. Some tools use snake_case (ai_visibility_check, bet_research), others use underscores in various patterns (generate_llms_txt, scan_competitor_ai_presence). The Shopify tools use a shopify_ prefix, but the rest follow no uniform scheme, making it unpredictable.

Tool Count1/5

35 tools is far too many for a server named 'Shopify'. Only 5 tools are Shopify-specific; the rest are general-purpose data tools (Pipeworx, Polymarket, memory, etc.). This severe scope mismatch makes the tool count inappropriate and overwhelming for the intended domain.

Completeness2/5

As a Shopify server, the tools cover only basic read operations (list/get products, orders, customers), missing crucial write operations (create, update, delete) and other Shopify features (webhooks, inventory, etc.). The general tools cover many domains but are not integrated into a coherent Shopify workflow.

Resources