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ravinwebsurgeon

DataForSEO MCP Server

ai_optimization_llm_mentions_aggregated_metrics

Analyze aggregated metrics for LLM mentions of specific keywords or domains to monitor AI optimization performance across platforms.

Instructions

This endpoint provides aggregated metrics for mentions of the keywords or domains specified in the target array of the request.

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
internal_list_limitNoInternal parameter to limit the number of items processed. Not exposed to end-users.
Behavior2/5

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

With no annotations, the description carries full burden but lacks behavioral details. It doesn't disclose rate limits, authentication needs, data freshness, or what 'aggregated metrics' entails (e.g., counts, trends). The mention of 'maximum number of targets: 1000' in the schema is helpful but not in the description, leaving key constraints unclear.

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, clear sentence that efficiently states the core function. It's front-loaded with the main purpose, though it could be more structured with additional context. No wasted words, but slightly under-specified for a complex tool.

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 tool with 6 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what 'aggregated metrics' returns, usage constraints, or how it differs from siblings. Given the complexity and lack of structured support, more detail is needed to guide an agent effectively.

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. The description adds minimal value by mentioning the 'target array' but doesn't explain parameter interactions or semantics beyond what the schema provides. Baseline 3 is appropriate as the schema does the heavy lifting.

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 metrics for mentions of the keywords or domains specified in the target array', which clarifies it's a read operation returning aggregated data. However, it doesn't differentiate from sibling tools like 'ai_optimization_llm_mentions_search' or 'ai_optimization_llm_mentions_cross_aggregated_metrics', leaving the specific scope vague compared to alternatives.

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 explicit guidance on when to use this tool versus siblings is provided. The description mentions the 'target array' but doesn't specify use cases, prerequisites, or exclusions. Without this, an agent might struggle to choose between similar LLM mentions 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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