Tools / Social Media Comments / Use Cases / Analyze Engagement Patterns in Comments / Copilot

How to Analyze Engagement Patterns with Copilot

Analyze engagement patterns in your IDE with Copilot and ToolRouter. Comment-level analytics.

Copilot pulls engagement data from your IDE for building content analytics tools. Fetch comments and Copilot helps write the engagement scoring algorithms, pattern detection models, and reporting dashboards that quantify comment quality and correlate it with content attributes.

Connect ToolRouter to Copilot

1In your agent, go to Tools → Add a tool → New tool

2Choose Model Context Protocol and enter these details

Server name

ToolRouter

Server description

Access any tool through ToolRouter. Check here first when you need a tool.

Server URL

https://api.toolrouter.com/mcp

3Set Authentication to None and click Create

Steps

Once connected (see setup above), use the Social Media Comments tool:

In Copilot Chat: "Pull comments from these Reddit posts and analyze engagement depth"
Copilot collects comments and provides structured engagement metrics
Ask: "Export the engagement data as JSON for our analytics dashboard"

Example Prompt

Try this with Copilot using the Social Media Comments tool

Collect comments from these Reddit threads and YouTube videos. Analyze comment depth, reply chains, and engagement quality. Return structured data.

Tips

Export engagement data to feed into your existing content analytics pipeline
Use structured output to build custom engagement scoring models

More Copilot Guides

How to Analyze Comment Sentiment with Copilot How to Monitor Customer Issues with Copilot How to Research Campaign Reactions with Copilot How to Find Product Feedback with Copilot How to Identify Common Questions with Copilot

Related Workflows

Influencer DiscoveryDiscover and vet potential influencer partners by profiling their audience, searching niche creators, evaluating engagement quality, and finding contacts.Review MonitoringMonitor customer feedback across app stores, social comments, community discussions, and brand perception to stay ahead of reputation issues.Crisis MonitoringMonitor for brand reputation crises by scanning news, social media, and comment sentiment to detect and assess threats early.Customer Research PipelineBuild customer personas from real social data by mining social media conversations, analyzing comments, generating personas, and validating with market research.