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CreatorDB

creatordb-mcp-server

Official
by CreatorDB

search_creators_nls

Search for creators across YouTube, Instagram, and TikTok using natural language queries. AI selects the platform and applies relevant filters.

Instructions

Search for creators using natural language across YouTube, Instagram, and TikTok. The AI determines the platform from your query and converts it into structured filters. Response includes creatorList AND a platform field telling you which platform was chosen. Best for exploratory queries where you don't know exact filter names — fall back to search_youtube/instagram/tiktok when you need precise control over filters. Credit cost is dynamic (token-based, typically 1–10 credits).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
descriptionYesNatural language search query, e.g. "Find US-based YouTube beauty creators with over 1M subscribers".
Behavior4/5

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

No annotations provided, so description carries full burden. Mentions dynamic credit cost (1-10 credits based on tokens) and that response includes a 'platform' field. Could be more explicit about read-only nature, but implied as a search tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Five sentences covering purpose, behavior, output, usage guidelines, and credit cost. Efficiently front-loaded with no redundant information.

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?

Given no output schema and one parameter, the description covers the essential aspects: input, output fields (creatorList, platform), dynamic credit cost, and fallback guidance. Could benefit from mentioning additional response fields, but sufficient for a simple tool.

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% for the single parameter 'description', which already has a good description. The tool description adds context about AI processing but doesn't significantly enhance parameter understanding beyond schema.

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?

The description clearly states the tool searches for creators using natural language across YouTube, Instagram, and TikTok. It distinguishes itself from sibling tools like search_youtube/instagram/tiktok by emphasizing its natural language interface and platform detection.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

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

Explicitly says 'Best for exploratory queries where you don't know exact filter names — fall back to search_youtube/instagram/tiktok when you need precise control over filters.' Provides clear when-to-use and when-not-to-use guidance.

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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