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Glama

Shopify

by io.github.pipeworx-io

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Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

The description adds significant behavioral context beyond the annotations: scoping by identifier, persistent vs. 24-hour retention based on authentication, and pairing with recall/forget. 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.

Conciseness5/5

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

The description is concise at three sentences, each earning its place: purpose, usage guidance, and behavioral details. Front-loaded with the primary action.

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 2 required parameters, full schema coverage, no output schema, and clear sibling relationships, the description fully covers what the agent needs to know: purpose, when to use, and behavioral nuances.

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

Parameters3/5

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

Schema coverage is 100% with clear descriptions for key and value. The description adds examples of key patterns and value types, but does not substantially extend the meaning beyond well-described schema parameters.

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: 'Save data the agent will need to reuse later'. It specifies the resource (key-value pairs) and the verb (save), and distinguishes from sibling tools like recall and forget by mentioning their pairing.

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 provides explicit guidance on when to use: 'Use when you discover something worth carrying forward'. It also mentions pairing with recall and forget, but does not explicitly state when not to use, though the purpose is clear from the context.

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

All Shopify toolsInspect
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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