AI & MODELS · MCP

Perplexity MCP

Give your AI agents a cited, live-web research tool through the Model Context Protocol

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Overview

Connecting Perplexity to autonomous agents over the Model Context Protocol turns grounded, real-time web research into a callable tool an agent can invoke mid-task. Instead of reasoning from stale training data, the agent asks Perplexity a question, receives a synthesized answer with citations, and can chain follow-ups — so its decisions rest on current, source-backed evidence.

What agents can do

Ask grounded questions

Agents send natural-language queries and receive synthesized, live-web answers with inline source citations attached.

Retrieve source URLs

Agents pull the underlying citation links so downstream steps can fetch, quote, and verify the original material.

Run scoped searches

Agents constrain queries by focus or domain and select a model to match speed or reasoning depth to the task.

Chain follow-up research

Agents issue iterative follow-ups in context to drill from a broad question into specific, decision-ready detail.

Agentic workflows we build

Self-updating research agent

We build agents that, before drafting or deciding, query Perplexity for current context, capture citations, and refuse to proceed on claims whose sources fail to resolve — keeping outputs both fresh and auditable.

Market-monitoring agent

A scheduled agent asks Perplexity about competitor moves, pricing, and category news, then routes cited summaries into Slack or a CRM so client teams act on verified changes.

Enrichment-in-the-loop workflows

Inside a larger automation, an agent calls Perplexity to enrich a lead or account with live firmographic detail, passing cited context to the next step in the pipeline.

INTEGRATIONBuilding with PerplexitySee the integration →THE PRACTICEAI & Machine LearningExplore the service →

FAQ

What does the MCP connection give an agent that a plain API call doesn't?

MCP exposes Perplexity as a standardized tool the agent can choose to call on its own, mid-reasoning, alongside its other tools. The agent decides when live research is needed rather than following a fixed, pre-scripted call.

How do you keep agent research trustworthy?

We design agents to keep and check citations: source URLs travel with each answer, verification steps confirm they resolve and support the claim, and sensitive or strategy-level outputs pass to a human before use.

Can you scope what an agent is allowed to ask or return?

Yes. We constrain queries with focus and domain filters, select models per task, and add guardrails and logging so the agent's research stays on-topic, cost-controlled, and reviewable.

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