Bangermap

io.github.AdesiaHQPublished by GitHub account AdesiaHQv1.0.3Updated Oct 24, 2026

Find YouTube outliers, videos that beat their channel's own baseline, on your own free API key.

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Overview

AI-generated overview

A local MCP server that uses your own YouTube Data API key to find videos outperforming their channel's baseline and to compare channels.

What you can do
Find a channel's outlier videos and how far each beats its own baseline, from a URL, @handle, channel id or video URL. Compute a channel's normal performance baseline, with Shorts and long-form measured separately. Compare several competitor channels, each judged against itself. Sweep a niche by following the channel graph from a seed channel to see what is overperforming there.
When to use it
Useful for YouTube content research: spotting ideas that worked on small channels, judging whether a view count is actually strong for its channel, or surveying competitors without keyword search.
Requirements
Runs locally over stdio via npx, so Node.js is needed. Requires a free YouTube Data API v3 key supplied in the YOUTUBE_API_KEY environment variable, with that API enabled in a Google Cloud project. Network access to Google's API; the free daily quota is about 10,000 units.
Before you install
You supply your own Google API key, which is passed in the environment and should be kept private. Quota is consumed per call, roughly 5 units per channel, and search is capped at 100 calls a day on a free key. The server reads public YouTube data only; the outlier multiples and baselines are its own calculations, not YouTube's.

Installation

In SourceWeft

  1. Open Bangermap in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

bangermap-mcp

YouTube outlier research over MCP, running on your own free YouTube Data API key.

An outlier is a video that beat its own channel's normal performance. A 10x on a small channel is a stronger signal than a million views on a channel that always gets a million, because it is proof an idea worked without an audience behind it. This server measures that, from any MCP client.

No account, no credits, no meter. The data comes from Google's public API on your key, so the only quota is the free 10,000 units a day Google already gives you, which is roughly 1,100 channel scans.

Setup

Create a free key in the Google Cloud console, enable YouTube Data API v3 on its project, then point your client at the server.

Claude Code:

sh
claude mcp add bangermap --env YOUTUBE_API_KEY=your-key -- npx -y bangermap-mcp

Claude Desktop, in claude_desktop_config.json:

json
{  "mcpServers": {    "bangermap": {      "command": "npx",      "args": ["-y", "bangermap-mcp"],      "env": { "YOUTUBE_API_KEY": "your-key" }    }  }}

Any other MCP client works the same way. The server speaks stdio and needs only YOUTUBE_API_KEY in its environment.

Tools

ToolWhat it answersCost
find_outliersWhich of this channel's recent uploads beat its own baseline, and by how much~5 units
channel_baselineWhat normal looks like for this channel, so a view count can be judged~5 units
compare_channelsWhat is working across a set of competitors, each measured against itself~5 units per channel
sweep_nicheWhich channels a seed channel is connected to, and what is overperforming on them~5 units per channel

Channels can be given as a URL, an @handle, a channel id, or the URL of any video on the channel.

Discovery runs through the channel graph rather than keyword search on purpose. Google caps search at 100 calls a day on a free key while channel reads cost a single unit, so seeding from a channel you already know is both cheaper and deeper.

How the baseline works

For each channel, the baseline is the median views of its recent uploads of the same format, counting only uploads old enough to have settled. Shorts and long-form get separate baselines, since comparing a 40-second Short against a 20-minute upload would flatter one and punish the other. A video's multiple is its views divided by that baseline.

The outlier multiple and the baseline are computed by this server, not by YouTube. View and channel data comes from the YouTube Data API. YouTube is a trademark of Google LLC.

The app

This server shares its scoring engine with Bangermap, a Mac app for the same research when you want it to persist. The app keeps a watchlist that re-scans and flags what is new since your last visit, shows outliers in a thumbnail wall for pattern study, exports to CSV, and caches locally so repeat research does not re-spend quota. One payment, no subscription, same free key.

There is also a free browser version at bangermap.com/tools/outlier-finder if you want to try the measurement without installing anything, and an n8n community node, n8n-nodes-bangermap, for the same operations inside a workflow.

Source: mcp/README.md at commit 0cc4e81

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v1.0.3LatestSep 16, 2026

Score

50% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts