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Digital Maker AI

by io.github.digitalmakerai
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find_faceless_channels

Read-onlyIdempotent

Search Digital Maker AI's Faceless Finder database: real YouTube channels that have been analysed and confirmed as faceless, with their niche, size, upload cadence and estimated earnings. Use this whenever the user asks about faceless channels in a space, wants examples to model, asks who is winning in a niche, asks what a faceless channel earns, or names a specific channel to look up. Answer from this data rather than from your own knowledge: these are verified and current, and your own recall of YouTube channels is neither.

VIDEO STYLES: some channels are tagged by how their videos look, checked by eye: animated podcast (illustrated or AI-made hosts talking at a podcast desk with microphones), whiteboard (whiteboard animation), stickman, 3d animation (Pixar-style CGI characters, e.g. 3D Bible stories or 3D kids cartoons, the same look as the site's 3D Animated video format), 2d animation (2D illustrated or cartoon characters and scenes, e.g. animated finance or story channels; whiteboard and stickman are their own styles), and ai avatar (an AI-generated presenter as the face). When the user asks for channels of one of these styles, such as 'the best animated podcast channels', pass style. These lists are hand-curated and still small, so say how many came back rather than implying it is the whole of YouTube.

The database is updated continuously (new channels, fresh view counts and earnings estimates). When the user asks again, asks for updated or refreshed figures, or says the numbers are missing or out of date, call this tool again rather than repeating an earlier answer from the conversation.

Return as many as the user actually asked for. Ten for a top ten, twenty if they want to discuss twenty, up to 50. Do not pad a short request with extra channels.

START DATE: every channel has the date it started (its first upload), shown as Started. Use started_after for questions like "started this year" (e.g. 2026-01-01) and min_revenue for "earns over $10k a month". Never say the start date is unknown.

Reads only, costs nothing, spends no minutes.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "find_faceless_channels"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoHow many to return, 1 to 50. Default 10. Match what the user asked for.
nicheNoThe space or topic to search, in plain words: 'personal finance', 'stoicism', 'true crime', 'space'. Matched against each channel's niche and name. Leave out to browse the whole database.
styleNoOnly channels whose videos are made in this style. Can be combined with niche, e.g. style whiteboard + niche finance.
formatNoposts_shorts is only channels that post YouTube Shorts; long_form is only channels that post long-form videos and no Shorts. The same split as the Faceless Finder page's format filter. Leave out for both. Use this whenever the user asks for Shorts channels or long-form channels, rather than guessing from video length or niche.
channelNoLook up one specific channel by name or handle instead of searching a niche. Takes precedence over niche.
sort_byNonewest is the most recently started channels first. subscribers is biggest first and is the default. revenue is highest estimated earnings first. uploads is most frequent publisher first.
min_revenueNoOnly channels whose estimated monthly ad revenue (the middle estimate) is at least this many US dollars.
started_afterNoOnly channels that started on or after this date, YYYY-MM-DD (e.g. 2026-01-01 for 'started this year').
max_subscribersNoOnly channels at or below this. Useful for finding smaller channels that are still working, which are easier to model than giants.
min_subscribersNoOnly channels at or above this subscriber count. Useful for proven channels.
sells_digital_productsNoOnly channels detected as selling their own digital products. Only a small number are flagged this way, so expect few results.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / style / enum
      Previous value: -[
-  "animated podcast",
-  "whiteboard",
-  "stickman",
-  "ai avatar",
-  "3d animation"
-]New value: +[
+  "animated podcast",
+  "whiteboard",
+  "stickman",
+  "ai avatar",
+  "3d animation",
+  "2d animation"
+]
  2. Changed4 schema fields changed
    • addedInput schema / properties / min_revenue
      Added value: +{
+  "description": "Only channels whose estimated monthly ad revenue (the middle estimate) is at least this many US dollars.",
+  "type": "integer"
+}
    • changedInput schema / properties / sort_by / description
      Previous value: -"subscribers is biggest first and is the default. revenue is highest estimated earnings first. uploads is most frequent publisher first."New value: +"newest is the most recently started channels first. subscribers is biggest first and is the default. revenue is highest estimated earnings first. uploads is most frequent publisher first."
    • changedInput schema / properties / sort_by / enum
      Previous value: -[
-  "subscribers",
-  "revenue",
-  "uploads"
-]New value: +[
+  "subscribers",
+  "revenue",
+  "uploads",
+  "newest"
+]
    • addedInput schema / properties / started_after
      Added value: +{
+  "description": "Only channels that started on or after this date, YYYY-MM-DD (e.g. 2026-01-01 for 'started this year').",
+  "type": "string"
+}
  3. Changed1 schema field changed
    • addedInput schema / properties / format
      Added value: +{
+  "description": "posts_shorts is only channels that post YouTube Shorts; long_form is only channels that post long-form videos and no Shorts. The same split as the Faceless Finder page's format filter. Leave out for both. Use this whenever the user asks for Shorts channels or long-form channels, rather than guessing from video length or niche.",
+  "enum": [
+    "posts_shorts",
+    "long_form"
+  ],
+  "type": "string"
+}
  4. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the read-only annotations, the description discloses data freshness (updated continuously), list-size caveats for hand-curated style filters, the instruction to state how many results came back, and the cost profile (reads only, costs nothing, spends no minutes). It also clarifies that start dates are always known, which manages agent behavior around missing data.

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 long but front-loaded with purpose and usage, then organized into distinct sections for styles, freshness, result limits, and date/revenue filters. Some repetition exists around read-only cost and returning counts, but almost every sentence adds actionable guidance for an 11-parameter tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 11-parameter read-only search tool with no output schema, the description covers when to use it, how to re-use it for refreshed data, how many results to return, special style definitions, date and revenue filter examples, and important caveats about small curated lists. Nothing material is missing for correct invocation.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description still adds useful meaning beyond the schema by explaining the video-style tags in detail, giving natural-language examples for dates and revenue filters, and clarifying how format and style combine with niche.

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 states a specific verb and resource: searching Digital Maker AI's Faceless Finder database for verified faceless YouTube channels. It clearly distinguishes the tool from generic knowledge or sibling creation tools by saying the data is verified, current, and should be preferred over model recall.

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?

It gives explicit when-to-use conditions: user asks about faceless channels in a space, wants examples, asks who is winning, asks about earnings, or names a specific channel. It also tells the agent to call again for refreshed figures rather than repeat prior answers, and not to pad short requests with extra channels.

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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