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mohamdben-yahia

YouTube MCP Server

discover_niche_sponsors

Identify brands actively sponsoring creators in your niche, including discount codes and URLs. Pitch these active sponsors once you reach 1k–5k views per video.

Instructions

Discover brands actively sponsoring creators in a niche with discount codes and URLs.

Reveals companies with active influencer marketing budgets in your niche so you can pitch them as soon as you reach 1k-5k views per video.

Args: niche_or_query: Niche topic or keyword (e.g. 'productivity apps', 'coding', 'fitness'). sample_videos: Number of top videos to inspect (10 to 30, default 20). region_code: Country code (default 'US').

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
region_codeNoUS
sample_videosNo
niche_or_queryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.1.0

TDQS

A4.5/5.0
Behavior4/5

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

There are no annotations, so the description carries the transparency burden for this read-only discovery tool. It discloses what results look like conceptually (sponsor brands, discount codes, URLs, active budgets) and hints at mechanism via 'Number of top videos to inspect.' It doesn't mention data-source caveats, but for a straightforward discovery tool this is solid coverage.

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?

The description is compact and front-loaded: a sharp lead sentence, a short purpose sentence, then a tight Args list. Every sentence earns its place and the formatting scannable.

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?

The description covers purpose, use case, and all parameters, and an output schema exists to cover return structure. It doesn't spell out when not to use the tool or name sibling alternatives, but the tool is simple and the main selection cues are present.

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

Parameters5/5

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

Input schema has no property descriptions (0% coverage), so the Args block is essential. It fully documents all three parameters: niche_or_query with examples, sample_videos with range and default, and region_code with default and meaning. This goes well beyond the raw schema and gives an agent everything needed to fill the arguments correctly.

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 opens with a specific verb and resource: 'Discover brands actively sponsoring creators in a niche' and further characterizes output with 'discount codes and URLs.' This distinguishes it from sibling tools like scout_niche_channels or search_videos, which target channels or videos rather than sponsor discovery.

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

Usage Guidelines4/5

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

The description gives a concrete use case: identify companies with active influencer marketing budgets so the creator can pitch them after reaching 1k-5k views per video. It doesn't explicitly list alternatives or exclusions, but the niche-focused context is clear enough for an agent to know when this tool fits.

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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