| name | researching-web |
| description | Web research via Perplexity AI. Use for technical comparisons (X vs Y), best practices, industry standards, documentation. Triggers on "research", "compare", "vs", "best practice", "which is better", "pros and cons". |
| context | fork |
| allowed-tools | ["Task","Read","Grep","Glob","WebFetch","mcp__perplexity-ask__perplexity_ask"] |
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:
- Review all cited URLs in the response
- WebFetch top 2-3 most relevant sources for deeper context
- 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]
