Perplexity AI Skill

AI-powered web search and reasoning capabilities via the scraper MCP server.

When to Use This Skill

• Searching the web with AI-synthesized answers
• Research tasks requiring multiple sources with citations
• Complex reasoning and multi-step analysis
• Questions about current events or real-time information
• Comparing options or analyzing trade-offs

Available Tools

Tool	Model	Best For
mcp__scraper__perplexity	sonar	General queries, quick searches, factual lookups
mcp__scraper__perplexity_reason	sonar-reasoning-pro	Complex analysis, comparisons, multi-step reasoning

Tool Usage

1. General Web Search

For straightforward queries and information lookup:

mcp__scraper__perplexity(
    messages=[
        {"role": "user", "content": "What are the latest features in Next.js 15?"}
    ],
    model="sonar",
    temperature=0.3,
    max_tokens=4000
)

Response includes:

• content: AI-synthesized answer with citation markers [1], [2], etc.
• citations: Array of source URLs
• model: Model used
• usage: Token statistics

2. Complex Reasoning

For analytical queries requiring deep thinking:

mcp__scraper__perplexity_reason(
    query="Compare React Server Components vs traditional SSR approaches. Consider performance, developer experience, and migration complexity.",
    temperature=0.3,
    max_tokens=4000
)

Use cases:

• Technology comparisons
• Architecture decisions
• Trade-off analysis
• Multi-factor evaluations

Parameters

Parameter	Type	Default	Description
messages	array	required	Conversation history (perplexity only)
query	string	required	Question to reason about (perplexity_reason only)
model	string	"sonar"	"sonar" or "sonar-pro"
temperature	number	0.3	Creativity 0-2 (lower = more focused)
max_tokens	integer	4000	Maximum response length

Conversation Format

The perplexity tool accepts conversation history:

mcp__scraper__perplexity(
    messages=[
        {"role": "system", "content": "You are a technical advisor."},
        {"role": "user", "content": "What database should I use for a real-time chat app?"},
        {"role": "assistant", "content": "For real-time chat, consider..."},
        {"role": "user", "content": "How does Redis compare to PostgreSQL for this use case?"}
    ]
)

Example Use Cases

Technology Research

mcp__scraper__perplexity(
    messages=[
        {"role": "user", "content": "What are the best practices for implementing authentication in Next.js 15 App Router?"}
    ]
)

Current Events

mcp__scraper__perplexity(
    messages=[
        {"role": "user", "content": "What are the latest AI model releases in 2025?"}
    ]
)

Product Comparison

mcp__scraper__perplexity_reason(
    query="Compare Vercel, Netlify, and AWS Amplify for deploying Next.js applications. Consider pricing, features, performance, and developer experience."
)

Architecture Decisions

mcp__scraper__perplexity_reason(
    query="Should I use a monorepo or polyrepo structure for a microservices architecture? Consider team size of 15 developers, 8 services, and CI/CD complexity."
)

Code Pattern Research

mcp__scraper__perplexity(
    messages=[
        {"role": "user", "content": "What is the recommended way to handle form validation in React 19 with server actions?"}
    ]
)

Security Research

mcp__scraper__perplexity_reason(
    query="Analyze the security implications of storing JWT tokens in localStorage vs httpOnly cookies. Consider XSS, CSRF, and token refresh patterns."
)

Best Practices

Choose the Right Tool

• perplexity: Factual queries, lookups, simple questions
• perplexity_reason: Comparisons, trade-offs, complex analysis

Temperature Settings

# Factual accuracy (recommended for most queries)
temperature=0.3

# More creative/exploratory responses
temperature=0.7

# Maximum creativity (use sparingly)
temperature=1.0

Working with Citations

Responses include numbered citations [1], [2], etc. matching URLs in the citations array:

{
    "content": "Next.js 15 introduces several features [1] including improved caching [2]...",
    "citations": [
        "https://nextjs.org/blog/next-15",
        "https://nextjs.org/docs/caching"
    ]
}

Iterative Research

Build on previous answers:

# Initial query
mcp__scraper__perplexity(messages=[
    {"role": "user", "content": "What is Zustand?"}
])

# Follow-up with context
mcp__scraper__perplexity(messages=[
    {"role": "user", "content": "What is Zustand?"},
    {"role": "assistant", "content": "Zustand is a state management library..."},
    {"role": "user", "content": "How does it compare to Redux Toolkit for a medium-sized application?"}
])

Requirements

The Perplexity tools require PERPLEXITY_API_KEY environment variable to be set on the scraper MCP server. Without it, these tools will not be available.