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Glama

AIsa SEO & AI Visibility

AI Keyword Data Keyword Search Volume

post_dataforseo_ai_keyword_volume_live
Destructive

This endpoint provides search volume data for your target keywords, reflecting their estimated usage in AI tools.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.7/5.0
Behavior2/5

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

Annotations already declare destructiveHint=true, readOnlyHint=false, and openWorldHint=true, so the agent knows this is a non-idempotent live call. The description adds nothing about cost/credit consumption, rate limits, or what makes a 'live' call different; it only describes the data semantics, not the tool's behavior. The read-flavored word 'provides' is mildly at odds with the destructive hint but does not rise to a contradiction.

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 with no filler or repetition. It is efficiently sized, though the brevity comes at the cost of substance rather than being a model of tight completeness.

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 a complex nested array body, no parameter guidance, and a crowded sibling set of similar live endpoints, one sentence is insufficient. The existence of an output schema excuses it from explaining return values, but it still needs to say how to call it and when to prefer it over batch_use or other live variants.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single body parameter is not explained at all in the description, and the measured schema description coverage is 0%. The description never mentions the keywords array, the location_name/location_code or language_name/language_code requirement, or the 1000-keyword limit, leaving the description to add no parameter meaning.

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?

The description states a specific verb and resource: it 'provides search volume data for your target keywords' and clarifies the data is estimated AI-tool usage, distinguishing it from generic SEO volume tools. It does not differentiate itself from the many sibling 'live' AI endpoints or from batch_use, so it stops short of 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 Guidelines2/5

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

There is no when-to-use guidance, no mention of prerequisites (location/language requirement), and no comparison to alternatives such as batch_use or the other live AI endpoints. The agent must infer usage entirely from the name.

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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