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by ai.howseen
OverviewDiscussionsAdmin

Suggest prompts to track

suggest_prompts
Read-only

Buyer questions worth tracking that the brand does not track yet: real questions from Hacker News discussions (ranked by engagement) plus AI suggestions for the category. Returns suggestions only; use add_prompts to track some. Limited to 20 calls per day.

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": "suggest_prompts"
  }
}

Input Schema

NameRequiredDescriptionDefault
brandNoBrand id, name or domain (from list_brands). Optional when the account has a single brand.
countNoHow many AI suggestions (default 10, max 25).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, destructiveHint=false and openWorldHint, so the safety profile is covered. The description still adds meaningful context beyond them: the 20-calls-per-day rate limit and confirmation that the call is non-mutating and must be followed by add_prompts to persist anything.

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?

Two tight sentences with zero waste; the content of the suggestions is front-loaded and the action/alternative and rate limit follow immediately.

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?

For a simple read-only suggestion tool with full schema coverage and no output schema, the description tells the agent what it returns and how to act on it. It is essentially complete, with only minor gaps like ordering/pagination of results.

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 both parameters are already fully documented (brand resolution and count with min/max/default). The description adds no syntax or format detail beyond the schema, so the baseline 3 applies.

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 title supplies the verb+resource ('Suggest prompts to track') and the description specifies exactly what the suggestions contain: real Hacker News buyer questions ranked by engagement plus AI suggestions for the category. It is clear, though the opening sentence describes the returned content rather than restating the action, so differentiation from siblings only emerges later.

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?

It explicitly routes the agent: 'Returns suggestions only; use add_prompts to track some,' naming the alternative and the follow-up action. The when-to-use condition (buyer questions the brand does not track yet) is clear, but there is no explicit when-not 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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