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ravinwebsurgeon

DataForSEO MCP Server

ai_optimization_llm_mentions_top_pages

Analyze aggregated LLM mentions to identify the most frequently referenced pages for specified domains or keywords, supporting SEO optimization by revealing content visibility in AI platforms.

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
links_scopeNospecifies which links will be used to extract domains and aggregation
initial_dataset_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
items_list_limitNomaximum number of results in the items array, min value is 1, max value is 10
internal_list_limitNomaximum number of elements within internal arrays, min value is 1, max value is 10
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure but offers minimal insight. It mentions 'aggregated metrics' but doesn't specify what metrics are returned, how pagination works (despite 'items_list_limit' and 'internal_list_limit' parameters), or any rate limits or authentication requirements. For a tool with 8 parameters and no output schema, this is inadequate for understanding the tool's behavior.

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 states the core purpose without unnecessary words. It is front-loaded with the main action ('provides aggregated LLM mentions metrics'), making it easy to parse. However, it could be more structured by breaking down key aspects, but it avoids verbosity.

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?

Given the tool's complexity (8 parameters, no annotations, no output schema, and rich sibling context), the description is incomplete. It doesn't explain the return format, what 'LLM mentions' entail, or how to interpret results. With no output schema and minimal behavioral context, the agent lacks sufficient information to use the tool effectively beyond basic parameter input.

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 the schema already documents all parameters thoroughly. The description adds no additional meaning beyond the schema, as it doesn't explain how parameters like 'target', 'initial_dataset_filters', or 'links_scope' interact to affect the output. Baseline 3 is appropriate since the schema does the heavy lifting, but the description fails to compensate with contextual insights.

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 for the specified target', which gives a general purpose but lacks specificity. It mentions 'aggregated metrics' and 'grouped by pages' but doesn't clarify what specific metrics are aggregated or what 'LLM mentions' refers to in practice. Compared to siblings like 'ai_optimization_llm_mentions_top_domains', it distinguishes by focusing on 'pages' rather than 'domains', but the distinction is minimal without more detail.

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. The description does not mention any prerequisites, exclusions, or specific contexts for usage. Given the complex sibling tools like 'ai_optimization_llm_mentions_search' or 'ai_optimization_llm_mentions_aggregated_metrics', the lack of differentiation leaves the agent guessing about appropriate use cases.

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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