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List tracked prompts

list_prompts
Read-only

The buyer questions (prompts) tracked for a brand, with per-prompt visibility, share of voice, position, which engines name the brand, and which brands and domains are cited.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoEnd date YYYY-MM-DD. Default: now.
fromNoStart date YYYY-MM-DD. Default: all time.
brandNoBrand id, name or domain (from list_brands). Optional when the account has a single brand.
limitNoMax items to return (default 50, max 200).
engineNoOptional AI engine filter, comma separated: chatgpt, gemini, perplexity, ai_overviews, ai_mode. Default: all engines.
offsetNoItems to skip, for pagination.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds value by naming the metric payload (visibility, share of voice, position, cited brands/domains), but says nothing about pagination behavior, defaults, or auth/brand requirements beyond what the schema states.

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?

One dense, front-loaded sentence with no filler, delivering the resource and its returned metrics efficiently. Slightly list-heavy but every element earns its place.

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 names the fields the tool returns, and the schema fully covers inputs. Given the read-only annotations and complete parameter coverage, an agent has enough to invoke it correctly, though pagination/ordering nuance is absent.

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 all six parameters (from/to, brand, limit, engine, offset) are fully documented in the schema itself. The description adds no parameter-level meaning, which is the expected baseline when the schema does the heavy lifting.

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?

States the resource (buyer questions/prompts tracked for a brand) and enumerates the per-prompt metrics returned, which meaningfully separates it from siblings like get_visibility or get_prompt_gaps. However, it reads as a noun phrase rather than an explicit verb+resource statement, so it's clear but not maximally directive.

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

Usage Guidelines2/5

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

No indication of when to use this versus the many sibling list/get tools (list_brands, get_visibility, get_prompt_gaps, search_responses). The single-sentence description gives the agent no routing guidance or prerequisites.

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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