Digital Maker AI

digital_product_trends

Read-onlyIdempotent

WHICH DIGITAL PRODUCTS SELL: every store in the Store Finder grouped by product type (digital planners, ebooks, spreadsheets, journals, workbooks, Notion templates, presets, templates), ranked, with how many stores sell each, the typical (median) estimated monthly revenue, how many make over $5k a month, the typical price and the top real examples. CALL THIS ONCE for questions like "what are the top digital products to sell", "what sells best", "what should I make" or "best digital products for 2027", then answer as a short ranked list from it. One call covers every product type, so do not call find_digital_stores per category. In Claude and ChatGPT it shows one compact panel. Costs nothing. Revenue is estimated.

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": "digital_product_trends"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 8, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the description need not restate safety; instead it adds genuinely new behavior: the call is free ("Costs nothing"), the revenue figures are modeled rather than actual ("Revenue is estimated"), and it renders as a single compact panel in Claude and ChatGPT. Those cost, data-quality and surface details are exactly the kind of context annotations cannot convey.

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 lead phrase is front-loaded in capitals and the key metrics come before the usage instructions, so an agent can skim the first clause and act. It is a long run-on with all-caps emphasis and a client-rendering remark that is marginal for tool selection, which keeps it short of a 5.

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?

With no input parameters and no output schema, the description carries the full burden of describing the return payload, and it does so precisely: grouping key, ranking, per-group store counts, median monthly revenue, a >$5k threshold count, typical price and example stores. An agent knows what it will receive and how to turn it into an answer.

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?

The tool takes zero parameters, and the description correctly spends no words on argument semantics. Baseline 4 applies for a no-parameter tool; nothing is added or missing on this dimension because there is nothing to document.

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 a specific verb and resource ("WHICH DIGITAL PRODUCTS SELL: every store in the Store Finder grouped by product type") and enumerates the exact product types and the exact metrics returned (store counts, median revenue, >$5k count, typical price, examples). It explicitly distinguishes itself from the sibling find_digital_stores, so an agent can choose between them without opening either schema.

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?

It gives explicit trigger phrasing for when to call ("what are the top digital products to sell", "what sells best", "what should I make", "best digital products for 2027") and an explicit anti-pattern ("do not call find_digital_stores per category"). The "CALL THIS ONCE" instruction plus the instruction on how to answer closes the loop on usage.

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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