Researching Web

Web Research with Perplexity

Two modes: quick (MCP direct) or deep (Task-based with context).

Best For

• Technology comparisons (X vs Y)
• Best practices, industry standards
• OWASP, security guidelines
• Documentation references
• Stable technical content

Quick Mode (Simple Queries)

Use MCP directly for fast, simple lookups:

mcp__perplexity-ask__perplexity_ask({
  "messages": [{ "role": "user", "content": "Your research question" }]
})

Deep Mode (Context-Aware Research)

For codebase-aware research, spawn the perplexity-researcher agent.

Foreground (blocking)

Task(subagent_type="perplexity-researcher", prompt="Research: <topic>")

Background (recommended for context efficiency)

Run in background to avoid polluting main context:

Task(
  subagent_type="perplexity-researcher",
  prompt="Research: <topic>",
  run_in_background=true
)

Retrieve results when ready:

TaskOutput(task_id="<agent_id>", block=true)

Use background mode when:

• Running multiple research queries in parallel
• Main task can continue while research runs
• Want to keep main context clean

When to Use Deep Mode

• User asks "best way to do X" (needs to compare with current code)
• Researching improvements to existing code
• Need to understand if recommendations apply to current stack

Query Formulation Tips

• Be specific: "Go 1.25 error handling best practices 2025"
• Include context: "Redis vs Memcached for session storage in Go services"
• Ask comparisons: "Pros and cons of gRPC vs REST for microservices"
• Include year: "Claude Code context optimization 2025"

Reference Following (Deep Research)

After Perplexity returns results with citations:

1. Review all cited URLs in the response
2. WebFetch top 2-3 most relevant sources for deeper context
3. Synthesize comprehensive answer combining all sources

# After Perplexity response with citations
WebFetch(url="<cited-url-1>", prompt="Extract key details about <topic>")
WebFetch(url="<cited-url-2>", prompt="Extract implementation examples")

Use reference following when:

• Initial answer is high-level and needs specifics
• User asks "tell me more" or "dig deeper"
• Implementing something that needs detailed guidance

Output Structure

## Summary

[Key findings - 2-3 sentences]

## Details

[Organized findings by topic]

## Recommendations

[Actionable items for the project]

## Sources

- [Source](url) - [what was learned]