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Shopmonkey MCP by usefulapi

Search inventory parts

shopmonkey_search_inventory_parts
Read-only

Search the parts inventory — name, part number, SKU, quantity on hand/available/reserved, retail and wholesale cost in cents, bin location, brand and vendor. Filter e.g. {"sku": "ABC-123"}. Shopmonkey: POST /v3/inventory_part/search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skipNoNumber of records to skip, for paging (use with limit).
limitNoMaximum number of records to return.
whereNoFilter object applied server-side, keyed by field name, e.g. {"lastName": "Smith"} or {"createdDate": {"gte": "2026-01-01"}}.
orderByNoSort instructions keyed by field name, e.g. {"createdDate": "desc"}.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. First observed

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds useful context by disclosing the underlying endpoint (POST /v3/inventory_part/search) and showing a filter shape, but it says nothing about result volume, pagination defaults, or cost-unit handling ('in cents' is stated for cost fields). Adequate but not rich.

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?

Front-loaded with what can be searched and what fields come back, followed by a filter example and the API path. No filler sentences, though the dense field enumeration is close to being a data dump rather than prose.

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

Completeness4/5

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

With no output schema, the description usefully compensates by listing the fields an agent can expect back, and 100% schema coverage handles the inputs. The main remaining gap is paging/return-size expectations for a search endpoint, which matters more given the absence of an output schema.

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 description coverage is 100%, so the four parameters (skip, limit, where, orderBy) are already documented in the schema. The description reinforces the where parameter with a concrete {"sku": "ABC-123"} filter example, but adds no guidance on paging or sorting beyond what the schema provides. Baseline 3 is appropriate.

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 gives a specific verb (Search) and resource (parts inventory) and enumerates the fields exposed — name, part number, SKU, quantities, retail/wholesale cost, bin, brand, vendor. This is well beyond a tautology. It does not need to differentiate from siblings since no other tool touches inventory parts, but it stops short of a 5 by not stating the distinguishing scope in one crisp clause.

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

Usage Guidelines3/5

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

Usage is implied by 'Search the parts inventory' plus the filter example, which shows an agent how to scope results. However, there is no explicit when-to-use, when-not-to-use, or reference to any alternative tool, and no mention of how to page through large result sets.

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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