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Get prompt gaps

get_prompt_gaps
Read-only

Tracked prompts where AI names at least one competitor but not the brand, per engine, with the competitors named and the domains cited. Sorted by search volume (when a cached figure exists) then by number of competitors named. Start here to see what to win back.

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_prompt_gaps"
  }
}

Input 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 25, max 100).
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

A4/5.0
Behavior4/5

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

Annotations already declare this a safe, closed-world read, so the safety burden is lifted. The description goes beyond them by disclosing the sort behavior (search volume when a cached figure exists, then count of competitors named) and the shape of each row, which is genuine behavioral context an agent cannot get from the annotations.

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 sentences with no filler; the core filter definition is front-loaded and the sorting rule is secondary, matching its importance. Every clause 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?

There is no output schema, and the description compensates well by describing row contents (prompts, named competitors, cited domains) and ordering. It omits only the default time window and pagination behavior, which are already covered by the parameter schema, so the gaps are minor.

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 from, to, brand, limit, engine, and offset are fully documented in the schema itself. The description adds nothing parameter-specific (no mention of date scoping, engine filter, or pagination semantics), so the baseline 3 is correct.

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 defines a precise, verifiable data set: tracked prompts where the AI names at least one competitor but not the brand, per engine, with competitors and cited domains included. That definition alone separates it from siblings like get_visibility, get_easiest_wins, and search_responses without the agent opening any schema.

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

Usage Guidelines3/5

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

"Start here to see what to win back" implies a use case and even a priority ordering among tools, which is more than nothing. But it names no alternatives and states no exclusion or prerequisite conditions (e.g. when to use get_easiest_wins or get_visibility instead), leaving the choice to inference.

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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