bangermap-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| YOUTUBE_API_KEY | Yes | Your free YouTube Data API key |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| find_outliersA | Rank a YouTube channel's recent uploads by how far each beat that channel's own baseline. Call this when the user wants to know which of a channel's videos overperformed, or wants proven ideas from a specific channel. Runs on the caller's own free YouTube Data API key. Costs about 5 quota units. |
| channel_baselineA | Report what normal performance looks like for a YouTube channel, as the median views of its recent settled uploads, with subscriber count and upload cadence. Call this when the user asks whether a view count is good for a given channel, or needs a reference point before judging a video. Costs about 5 quota units. |
| compare_channelsA | Scan several YouTube channels at once and return the strongest outliers across all of them, each measured against its own channel's baseline. Call this when the user wants to see what is working across a niche or a set of competitors rather than one channel. Costs about 5 quota units per channel. |
| sweep_nicheA | Find channels related to a seed channel through the channels it features, then rank the strongest outliers across all of them. Call this when the user wants to discover channels in a niche they do not already know, rather than analyse ones they named. Costs about 5 quota units per channel found. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool addresses a distinct task: baseline establishes a reference, find_outliers analyzes a single channel, compare_channels handles multiple named channels, and sweep_niche discovers unknown channels. There is no overlap in purpose or output.
Three tools follow the verb_noun pattern (find_outliers, compare_channels, sweep_niche), while channel_baseline deviates by using a noun_noun structure. The names remain readable and the pattern is mostly consistent.
Four tools is a tight, well-scoped set for a niche YouTube analysis server, each serving a clear part of the workflow from baseline to niche discovery without redundancy.
The server covers the full analysis lifecycle: establishing a baseline, identifying outliers, comparing across channels, and expanding to unknown channels. No obvious missing operations for its stated purpose.