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ravinwebsurgeon

DataForSEO MCP Server

ai_optimization_llm_mentions_search

Analyze aggregated LLM mentions metrics for domains or keywords to identify frequently referenced pages and optimize AI-related content strategies.

Instructions

This endpoint provides aggregated LLM mentions metrics grouped by the most frequently mentioned pages for the specified target

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetYesArray of target objects to search for LLM mentions. Each object must contain either 'domain' or 'keyword'. Maximum number of targets: 1000
location_nameNofull name of the location, example: 'United Kingdom', 'United States'
language_codeNoSearch engine language code (e.g., 'en')
platformNoPlatform to search for LLM mentions
filtersNoyou can add several filters at once (8 filters maximum) you should set a logical operator and, or between the conditions the following operators are supported: regex, not_regex, <, <=, >, >=, =, <>, in, not_in, match, not_match, ilike, not_ilike, like, not_like you can use the % operator with like and not_like, as well as ilike and not_ilike to match any string of zero or more characters merge operator must be a string and connect two other arrays, availible values: or, and. example: ["ai_search_volume",">","1000"] The full list of possible filters is available in 'ai_optimization_llm_mentions_filters' tool
order_byNoresuresults sorting rules optional field you can use the same values as in the filters array to sort the results possible sorting types: asc – results will be sorted in the ascending order desc – results will be sorted in the descending order you should use a comma to set up a sorting type example: ["ai_search_volume,desc"] note that you can set no more than three sorting rules in a single request you should use a comma to separate several sorting rules example: ["ai_search_volume,desc"] The full list of possible orders is available in 'ai_optimization_llm_mentions_filters' tool
limitNoNumber of results to return. Default is 10, maximum is 1000.
Behavior2/5

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

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions aggregation and grouping by pages but doesn't disclose critical traits like whether this is a read-only operation, potential rate limits, authentication needs, data freshness, or what the output format looks like (especially problematic without an output schema).

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?

The description is a single, efficient sentence that clearly states the core function. It's appropriately front-loaded with the main purpose, though it could benefit from slightly more detail given the tool's complexity. There's no wasted verbiage.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with 7 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what 'aggregated LLM mentions metrics' actually includes, how grouping works, what the output looks like, or any behavioral constraints. The agent would struggle to use this effectively without significant guessing.

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 parameters are well-documented in the schema itself. The description adds no additional parameter context beyond implying the 'target' parameter is central. Since the schema does the heavy lifting, the baseline score of 3 is appropriate despite the description's lack of parameter elaboration.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool provides 'aggregated LLM mentions metrics grouped by the most frequently mentioned pages' which clarifies it's a search/analysis tool for LLM mentions with grouping. However, it doesn't distinguish this from sibling tools like 'ai_optimization_llm_mentions_top_pages' or 'ai_optimization_llm_mentions_aggregated_metrics', leaving ambiguity about their functional differences.

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 guidance is provided on when to use this tool versus alternatives. With multiple sibling tools focused on LLM mentions (e.g., aggregated_metrics, top_domains, top_pages), the description lacks any context about appropriate use cases, prerequisites, or comparisons to help an agent choose correctly.

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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