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SLAM Commerce MCP

SLAM Commerce MCP

by valljo888ai
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slam_products_count

Count products in a Shopify store with optional filters for status, vendor, or product type to track inventory and analyze catalog data.

Instructions

Returns the total count of products, optionally filtered by status, vendor, or product_type.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "slam_products_count"
  }
}

Input Schema

NameRequiredDescriptionDefault
statusNoFilter by product status (e.g. 'ACTIVE')
vendorNoFilter by vendor name
product_typeNoFilter by product type

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

B3.1/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states it 'Returns the total count' but doesn't cover aspects like whether this is a read-only operation, performance implications, rate limits, or authentication requirements. For a tool with no annotations, this leaves significant behavioral gaps.

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 a single, efficient sentence that front-loads the core purpose ('Returns the total count of products') and succinctly adds filtering details. There is no wasted language, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (simple counting with optional filters), no output schema, and no annotations, the description is adequate but incomplete. It covers the basic purpose and parameters but lacks behavioral context and usage guidance, which are important for effective tool selection by an AI agent.

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?

The description mentions optional filtering by status, vendor, or product_type, which aligns with the three parameters in the schema. Since schema description coverage is 100%, the schema already documents these parameters well, so the description adds minimal value beyond restating what's in the schema, meeting the baseline score.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Returns') and resource ('total count of products'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'slam_products_list' or 'slam_products_search', which likely return detailed product data rather than just counts.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It mentions optional filtering but doesn't specify scenarios where counting products is preferred over listing them, nor does it reference sibling tools for comparison, leaving usage context unclear.

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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