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Get fan-out queries

get_fanout_queries
Read-only

The web searches the AI engines ran behind each tracked question (latest scan): the queries to rank for so the AI finds the brand, overall and per question.

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_fanout_queries"
  }
}

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 30, max 200).
engineNoOptional AI engine filter, comma separated: chatgpt, gemini, perplexity, ai_overviews, ai_mode. Default: all engines.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare this is a non-destructive, closed-world read, so the description doesn't need to restate safety. It does add one useful behavioral fact — results reflect the *latest scan* and are broken down overall and per question — but says nothing about pagination/truncation behavior or whether history is available.

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?

A single front-loaded sentence that identifies the resource, freshness, and grouping without filler. Slightly dense phrasing ('so the queries to rank for so the AI finds the brand') costs a little clarity but nothing is wasted.

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 and fully documented inputs, the description covers what the agent needs to select and call: what data, which scan, and the overall/per-question granularity. A brief note on response shape or how empty results behave would make it complete.

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 coverage is 100%: all five parameters carry descriptions (date range, brand resolution, limit up to 200, engine list). The prose adds no parameter-level detail beyond what the schema already states, so the baseline 3 holds.

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 a specific resource — the web searches (fan-out queries) AI engines ran behind each tracked question, scoped to the latest scan and returned overall and per question. The verb 'get' plus the precise resource makes the output recognizable. It doesn't explicitly distinguish itself from nearby siblings like get_top_sources or search_responses, which keeps it from a 5.

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?

Usage is implied by the phrase 'the queries to rank for so the AI finds the brand,' which hints at the SEO/AEO use case, but there is no explicit when-to-use rule or named alternative. The agent must infer that this differs from visibility or source-level tools.

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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