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keepa-mcp

keepa-mcp

by purahmanian
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Get Deals

get_deals
Read-onlyIdempotent

Locate Amazon products with significant price drops by setting minimum discount percentage and rating thresholds, enabling targeted deal hunting.

Instructions

Fetch current Amazon price-drop deals from Keepa.

Returns products where the price has dropped significantly, filtered by minimum price drop percentage and minimum rating.

Args:

  • min_price_drop_pct (number, optional): Minimum % price drop (1-99). Default: 20

  • min_rating (number, optional): Min rating 0-50 (45 = 4.5 stars). Default: 40

  • page (number, optional): Page offset for pagination. Default: 0

  • domain (number, optional): Keepa domain id (default 1 = amazon.com)

Examples:

  • "Show me deals with at least 30% off" -> min_price_drop_pct=30

  • "Find highly-rated products on sale" -> min_rating=45, min_price_drop_pct=20

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": "get_deals"
  }
}

Input Schema

NameRequiredDescriptionDefault
pageNoPage offset for pagination. Default: 0 (first page)
domainNoKeepa domain id (1 = amazon.com)
min_ratingNoMinimum product rating (0-50, where 45 = 4.5 stars). Default: 40
min_price_drop_pctNoMinimum price drop percentage to include. Default: 20

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4.2/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior, so the description's additional context about returning products with significant price drops and filtering by rating is useful. It does not describe return structure, sorting, or edge cases, but given strong annotations, the bar is lower.

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 compact, front-loaded with purpose, and organized with an Args list and examples. Every sentence serves a purpose; no fluff or redundancy.

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?

Given the simple parameter set (all optional, no enums) and the lack of an output schema, the description covers the essential context: purpose, filters, defaults, pagination, and domain selection. It could mention return format or sorting, but is largely complete for practical use.

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?

Schema covers all four parameters with descriptions, but the tool description adds practical mapping examples ('Show me deals with at least 30% off' → min_price_drop_pct=30), which helps an agent translate natural language into parameter values. This goes beyond the schema's terse 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 opens with 'Fetch current Amazon price-drop deals from Keepa', clearly identifying the action and resource. It distinguishes itself from sibling tools like get_best_sellers and search_products by focusing specifically on price-drop deals with rating and drop-percentage filters.

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 gives clear context for when to use the tool (to find products with significant price drops) and provides concrete examples of user queries. However, it does not explicitly compare with alternatives or state when not to use this tool, so it falls just short of full guidance.

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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