| name | perplexity |
| description | Optional AI-synthesized web search via Perplexity MCP. Returns AI-grounded answers with inline citations rather than raw search results — best for synthesis-heavy questions, current consensus queries, and cross-validation of research findings. Non-blocking — falls back to other search tools if unavailable. |
| location | managed |
| context | fork |
| allowed-tools | ["ToolSearch","mcp__perplexity__search"] |
Perplexity MCP — Optional AI-Synthesized Search
This skill wraps the Perplexity MCP server. Unlike raw web search tools that return a list of URLs, Perplexity returns AI-synthesized answers with inline citations — it reads the web for you and summarizes what it finds.
Best for: "What is the current consensus on X?", "Compare A vs B", "What changed in X since version Y?"
Not ideal for: Retrieving specific raw URLs, domain-specific technical documentation, anything requiring exact source control
Setup: Requires the Perplexity MCP server and a Perplexity API key.
claude mcp add -s user perplexity npx @perplexity-ai/mcp-server
Pre-Flight: Check Availability
ToolSearch: "perplexity"
→ Returns: mcp__perplexity__search
→ If found: proceed to Step 1
→ If not found: return availability: "unavailable", skip — other search tools handle raw web
Non-blocking: Perplexity is a complement to web search, not a replacement. If unavailable, research continues normally with Brave Search and other tools.
Step 1: Identify High-Value Query Types
Perplexity shines for these query patterns:
high_value_use_cases:
consensus_synthesis:
description: "What does the community/industry currently think about X?"
examples:
- "What is the current consensus on Go vs Rust for systems programming?"
- "What are the most common criticisms of event sourcing in practice?"
why: Returns synthesized view across many sources, not just one opinion
comparison_analysis:
description: "Compare A vs B across multiple criteria"
examples:
- "Compare Redis vs Memcached for session storage in 2025"
- "Compare Kafka vs RabbitMQ for event streaming at scale"
why: AI synthesis better at multi-dimensional comparison than individual sources
current_state_snapshot:
description: "What is the current state of X?"
examples:
- "What is the current state of WebAssembly browser support in 2025?"
- "What authentication standards are recommended in 2025?"
why: Perplexity indexes recent content and synthesizes the current picture
cross_validation:
description: Use to validate or challenge findings from other sources
examples:
- "Are there known limitations or criticisms of [finding from web search]?"
why: Provides a second synthesis pass to surface what raw search might miss
Not ideal for:
- Retrieving specific URLs or raw source lists (use Brave Search)
- Deep technical documentation (use context7 or direct docs)
- Codebase-specific questions (use DeepWiki)
Step 2: Execute Query
mcp__perplexity__search(query="your synthesis question here")
Query construction tips:
- Frame as a question requiring synthesis: "What is...", "How does... compare to...", "What are the tradeoffs of..."
- Include context: "in production Go services", "for startups in 2025"
- Ask for recency: "currently", "as of 2025", "latest recommendations"
- Request specific framing: "from a security perspective", "in terms of developer experience"
Step 3: Process Response
Perplexity returns a synthesized answer with inline citations. Process it as:
{
"source": "perplexity",
"query": "the query executed",
"answer": "Synthesized answer text with [citation] references",
"citations": [
{
"index": 1,
"url": "https://source-url",
"title": "Source title"
}
],
"credibility": "MEDIUM",
"type": "ai-synthesis",
"key_points": ["Extracted key point 1", "Extracted key point 2"]
}
Credibility note: Always tag Perplexity outputs as credibility: MEDIUM — the underlying sources may be HIGH, but the synthesis layer introduces potential for hallucination. Use inline citations to verify critical claims.
Calling Context Integration
When invoked by deep-research
Complement domain researcher queries. Run Perplexity in parallel with Brave Search for synthesis-heavy topics. Pattern:
- Domain researcher runs Brave Search for raw source collection
- Perplexity runs for consensus synthesis on the same topic
- Cross-reference: does Perplexity's synthesis align with the raw sources?
- Discrepancies become noted contradictions or gaps in research findings
When invoked for cross-validation
After primary research is complete, run Perplexity with: "What are the main criticisms or limitations of [primary finding]?" — surfaces counter-perspectives that raw search might have missed.
When invoked standalone
Execute 1-3 synthesis queries, return structured answer with citations. Suitable for quick orientation on an unfamiliar topic before deeper research.
Output
{
"skill": "perplexity",
"availability": "available | unavailable",
"queries_executed": ["list of queries"],
"results": [
{
"query": "...",
"answer": "...",
"citations": [...],
"key_points": [...]
}
],
"validation_note": "AI-synthesized answers — verify critical claims via inline citations"
}
If unavailable:
{
"skill": "perplexity",
"availability": "unavailable",
"reason": "MCP server not configured",
"setup_hint": "claude mcp add -s user perplexity npx @perplexity-ai/mcp-server",
"alternative": "Use brave-search or web-search-prime for raw web results"
}
Why Perplexity vs Other Search Tools?
| Tool | Returns | Best for |
|---|
brave-search | Raw web results with URLs | Source collection, specific URL retrieval |
perplexity | AI synthesis with citations | Consensus questions, comparison, current state |
web-search-prime | Raw web results | General fallback search |
deepwiki | Codebase wiki answers | Codebase-specific questions |
Perplexity and Brave Search are complementary, not competing — run both for comprehensive research coverage.
