Perplexity AI MCP. Stop Guessing. Start Quoting Sources.

Perplexity AI connects real-time web search and source citation retrieval straight to your agent. You ask questions, get answers grounded in current facts, and run deep reports using verifiable sources for every claim. This server gives you citations for everything it pulls from the live internet.

When you use this server, your AI client runs specialized requests through dedicated tools. The system queries the live web, synthesizes what it finds, and returns an answer that links back to the original source URLs. You'll never have to jump between a search engine tab and your chat window again.

Getting Quick, Cited Answers

Use chat_completion for simple questions; you get an immediate answer with citations using basic query logic. If you need absolute certainty that every single fact is linked back to its original source URL, run chat_with_citations. For a full search result—including citations, related images, and suggested follow-up questions all at once—just use search_query.

Deep Research & Analysis

Need more than just an answer? Run deep_research for an exhaustive web search that generates a detailed, long-form report with thorough citation tracking. For complex logic tasks like math proofs or multi-step code reviews, use the reasoning tool to perform step-by-step analysis via logic chains. You can also force specialized context and role-playing by setting parameters using system_prompt_query, which tells the model exactly what job it's doing (like 'financial expert').

Filtering Sources and Time Periods

Want to trust your data source? Use chat_with_domain_filter to restrict search results only to a specific list of domains you provide, maybe just government sites or academic journals. If the timing matters—say, you need info on last week's market shift—run chat_with_recency_filter, which filters results by time period (hour, day, week, month).

Maintaining Context and Data Integrity

You don't have to repeat yourself. If you ask a follow-up question, the agent remembers the whole conversation history because of follow_up or chat_with_history. For visuals, run chat_with_images, which gets search results including relevant images and their associated URLs alongside the text answer. You can also generate suggested next steps for further research by calling chat_with_related_questions after you get your initial answer.

When you need the output to be programmatically usable, use structured_query. This forces Perplexity AI to return data that matches a precise JSON schema you define. For basic model introspection, run list_models to see all available models before starting your query.

What You Get When You Use It:

• You get an immediate answer with citations using the basic query tool (chat_completion).
• You run extensive searches and generate full reports on complex topics, tracking every citation (deep_research).
• You restrict sources to trusted websites or academic domains (chat_with_domain_filter).
• The agent remembers previous conversation history for continuous questioning (follow_up / chat_with_history).
• You force the model to output data that matches a specific schema, making it ready for code ingestion (structured_query).
• You analyze complex logic chains like mathematical proofs or step-by-step code analysis (reasoning).