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x402vid AI Intelligence & Channel Growth for YouTube Videos

Keyword Autocomplete Miner

keyword_autocomplete
Read-only

Scrape YouTube autocomplete suggestions for any seed topic (better than $0.06 rivals).

Multi-seed expansion (how/best/vs/tutorial/review/beginners/year), frequency-ranked with intent clusters: questions, tutorials, comparisons, year-tagged trends, buy intent. Keyless, no AI — upstream of niche_research.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoLanguage code (default en)en
regionNo2-letter region code (default US)US
x_paymentNoBase64 x402 payment payload (required — pass the X-PAYMENT value as an argument over MCP)
seed_topicYesSeed keyword(s), comma-separated for multi-seed (e.g. sourdough, or "a, b")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / seed_topic / description
      Previous value: -"Seed keyword (e.g. sourdough, sneakers)"New value: +"Seed keyword(s), comma-separated for multi-seed (e.g. sourdough, or \"a, b\")"
  2. Added

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and non-destructive/non-idempotent behavior, so the bar is lower. The description still adds real context beyond them: keyless and AI-free execution, multi-seed expansion across modifiers, and frequency-ranked output grouped into intent clusters, which tells the agent what the call actually produces.

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?

Compact and front-loaded, with the core action and the sibling positioning leading. The parenthetical '(better than $0.06 rivals)' is competitive marketing noise that does not help an agent decide or invoke, so it is not quite pristine.

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 an output schema present, return values need not be explained, and the description covers pipeline position, expansion behavior, and output grouping. It omits any mention that a payment payload is required to call it, but that gap is filled by the schema's x_payment description.

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%, so lang, region, x_payment, and seed_topic are already documented. The description only reinforces seed-topic behavior (comma-separated multi-seed, modifier expansion) and adds no syntactic detail beyond the schema, making 3 the correct baseline.

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

Purpose5/5

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

States a specific verb+resource (scrape YouTube autocomplete suggestions for a seed topic) and positions itself relative to a sibling by calling itself 'upstream of niche_research'. An agent can distinguish this from trending_on_youtube or niche_research without opening a schema.

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?

The phrase 'upstream of niche_research' implies a pipeline position, which gives some when-to-use signal, but there is no explicit statement of when to prefer this over siblings like trending_on_youtube or search_and_rank_videos, and no exclusions. Usage is inferred rather than stated.

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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