Perplexity MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| PERPLEXITY_MODEL | No | The Perplexity model to use: sonar-reasoning-pro (most capable with enhanced reasoning), sonar-reasoning (enhanced reasoning), sonar-pro (faster response times), or sonar (default) | sonar |
| PERPLEXITY_API_KEY | Yes | Your Perplexity API key from https://www.perplexity.ai/settings/api |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| searchC | Search the web using Perplexity AI |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools, making disambiguation perfect. The tool's purpose is clearly defined as web search using Perplexity AI, leaving no room for misselection.
A single tool named 'search' follows a consistent and straightforward verb-based naming pattern. There are no other tools to compare against, so no inconsistency can exist in the naming scheme.
One tool is too few for a server named 'Perplexity MCP Server', which implies broader capabilities beyond just search. A single tool feels thin and under-scoped for what might be expected from a Perplexity AI integration, such as additional operations like summarization or follow-up queries.
The tool surface is severely incomplete for a web search domain; it only offers a basic search function without any complementary tools for refining results, getting summaries, or handling related queries. This creates significant gaps that could lead to agent failures in complex tasks.