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.
| Name | Required | Description | Default |
|---|---|---|---|
| analysis_id | Yes | The numeric analysis ID from NetworkLytics |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses authentication requirements (API key via environment variable) and return behavior (structured data similar to get_shared_analysis), which adds useful context. However, it lacks details on error handling, rate limits, or other behavioral traits like response format specifics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized with two sentences that are front-loaded with key information (retrieval action and authentication). It avoids unnecessary details, though it could be slightly more structured by separating usage guidelines from behavioral notes.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is somewhat complete by covering purpose and authentication. However, it lacks output details (e.g., what the structured data includes) and doesn't fully address sibling tool differentiation, leaving gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the schema already documents the single parameter 'analysis_id' as a numeric ID from NetworkLytics. The description adds no additional meaning beyond this, such as format examples or constraints, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Retrieve') and resource ('YouTube comment network analysis by its ID'), making the purpose understandable. However, it doesn't explicitly distinguish this tool from its sibling 'get_shared_analysis' beyond noting they return similar data, missing a direct comparison of when to use each.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by specifying that it retrieves by ID and requires API key authentication, but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'get_shared_analysis' or 'get_api_info'. No exclusions or clear alternatives are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get NetworkLytics API information and available endpoints.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While 'Get' implies a read-only operation, it doesn't specify authentication requirements, rate limits, error conditions, or what format the API information is returned in. The description is minimal and lacks important operational context that would help an agent use this tool effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that states exactly what the tool does with no wasted words. It's appropriately sized for a zero-parameter tool and front-loads the essential information without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with no output schema, the description is minimally complete in stating what information will be retrieved. However, it lacks context about what 'API information' includes (version, endpoints, capabilities) and how this tool relates to the sibling analysis retrieval tools. The absence of annotations means the description should provide more behavioral context than it does.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters with 100% schema description coverage, so the baseline for this dimension is 4. The description appropriately doesn't discuss parameters since none exist, which is correct and efficient.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('Get') and resource ('NetworkLytics API information and available endpoints'), making it immediately understandable. However, it doesn't explicitly distinguish this from its siblings (get_analysis_by_id, get_shared_analysis), which appear to be more specific data retrieval tools rather than metadata endpoints.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention whether this should be used for initial API discovery, health checking, or as a prerequisite for other operations. With sibling tools that retrieve specific analyses, there's no indication of the relationship or sequencing between them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.1get_analysis_by_idget_api_infoget_shared_analysisScored 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.
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Social media data: 85 tools across 11 platforms (YouTube, TikTok, Instagram, X & more), one key.
