Provides tools for analyzing YouTube comment networks, including retrieving analysis results by shared links or analysis IDs, identifying key influencers, and examining community sentiment trends in YouTube comment sections.
NetworkLytics MCP (Model Context Protocol) server.
You can directly query YouTube comment network analysis results in MCP-supported AI tools such as Claude Desktop and Cursor.
pip install networklytics-mcpRelated MCP server: vidlens-mcp
Add to ~/.claude/claude_desktop_config.json or %APPDATA%\Claude\claude_desktop_config.json:
{
"mcpServers": {
"networklytics": {
"command": "networklytics-mcp",
"env": {
"NETWORKLYTICS_API_URL": "https://networklytics.com",
"NETWORKLYTICS_API_KEY": "nly_your_api_key_here"
}
}
}
}You can obtain your API key from the NetworkLytics account settings page.
You can make requests in Claude such as:
"Show me the analysis results for this shared link: https://networklytics.com/shared/abc-123-..."
"Find the top 3 key influencers from the analysis results"
"What are the sentiment trends in this YouTube channel's comment community?"
Tool | Description | Authentication |
| Retrieve analysis results using a shared link token | Not required |
| Retrieve results using an analysis ID | API key required |
| API information and list of endpoints | Not required |
{
"video": { "title": "...", "channel": "...", "view_count": 123456 },
"network": {
"total_nodes": 1500,
"total_edges": 3200,
"density": 0.003,
"community_count": 7
},
"sentiment": {
"positive_ratio": 0.62,
"negative_ratio": 0.15,
"overall": "positive"
},
"top_influencers": [
{ "author": "username", "degree_centrality": 0.12, "comment_count": 45 }
],
"topic_keywords": ["키워드1", "키워드2"],
"ai_insights": { ... }
}Retrieve a YouTube comment network analysis by its ID. Requires API key authentication (set NETWORKLYTICS_API_KEY environment variable). Returns the same structured data as get_shared_analysis.
Inputs: analysis_id (required).
Get NetworkLytics API information and available endpoints.
Inputs: no named parameters.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 3 tools
Each tool has a clearly distinct purpose: get_analysis_by_id retrieves a specific analysis by ID with authentication, get_api_info provides API metadata, and get_shared_analysis fetches public analysis from a share link. There is no overlap or ambiguity in their functions.
All tools follow a consistent verb_noun pattern with snake_case (get_analysis_by_id, get_api_info, get_shared_analysis). The naming is predictable and uniform throughout the set.
With only 3 tools, the set feels thin for a network analytics server. It lacks essential operations like creating, updating, or deleting analyses, which limits functionality and makes the server seem incomplete for its domain.
The server is severely incomplete for network analytics. It only provides retrieval and info tools, missing core operations such as initiating analyses, managing data, or performing CRUD actions. This will cause significant agent failures in handling typical workflows.
Observatory of public media reception: classify YouTube comments for mood, support and controversy.
Public YouTube comments and linked replies, Top/Newest sorting, likes and video metadata.
YouTube transcripts, comment sentiment and the moments viewers react to, plus channel stats.
YouTube, TikTok, Instagram, X, Reddit, LinkedIn and Threads: public data in one schema.
