CueScout

List the tracked prompts and how each engine answered

list_prompts

Read-only

Use this when someone asks which questions CueScout tracks for their brand, which ones the brand is missing from, or who gets named instead. Returns each tracked prompt with the latest verdict per engine (named and cited, named, or not named), the brand's mention and citation rate over the window, and the brands named instead.

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": "list_prompts"
  }
}

Input Schema

Name	Required	Description	Default
`days`	No	The window to report on, in days. Defaults to 28.
`brand`	No	Which brand, by name or id, when the account tracks more than one. Leave empty to use the only brand; list_brands shows them all.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 14, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Annotations already establish the safe read-only, non-open-world profile. The description adds genuinely useful behavioral context beyond them: the per-engine verdict taxonomy (named and cited / named / not named) and the aggregate mention and citation rate over a window. No auth or rate-limit detail, but with no output schema this return-shape disclosure is valuable.

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?

Two sentences, front-loaded with the usage trigger followed by the return summary. The parenthetical verdict enumeration is compact and earns its place, though the sentence is somewhat dense.

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 steps in to enumerate the returned fields (per-engine verdicts, mention/citation rates, competing brands), and both parameters are fully documented in the schema. What remains thin is routing to sibling tools for adjacent questions, but nothing required to call it correctly is missing.

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%, with 'days' enum-documented (7/28/90, default 28) and 'brand' explaining the name-or-id and empty-fallback behavior along with the list_brands pointer. The description only alludes to "over the window" and adds nothing 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 and description give a specific verb+resource: lists each tracked prompt with the latest per-engine verdict, plus the brand's mention/citation rate and competing brands named. It is clearly distinct in substance from siblings like get_prompt_answers or list_citation_sources, but it never names or contrasts those siblings explicitly.

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?

Gives concrete trigger conditions ("Use this when someone asks which questions CueScout tracks... which ones the brand is missing from... who gets named instead"). It stops short of exclusions or pointing to an alternative sibling for related questions, so it is clear context without true 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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