Perplexity AI MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| PERPLEXITY_API_KEY | Yes | Your Perplexity API key |
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 |
|---|---|
| chat_perplexityA | Maintains ongoing conversations with Perplexity AI. Creates new chats or continues existing ones with full history context. |
| searchC | Perform a general search query to get comprehensive information on any topic |
| get_documentationB | Get documentation and usage examples for a specific technology, library, or API |
| find_apisC | Find and evaluate APIs that could be integrated into a project |
| check_deprecated_codeC | Check if code or dependencies might be using deprecated features |
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 5 tools
Most tools have distinct purposes: chat_perplexity handles conversational AI, check_deprecated_code analyzes code deprecation, find_apis discovers APIs, get_documentation retrieves docs, and search performs general queries. However, get_documentation and search could overlap slightly when users seek technical information, but descriptions clarify their focus.
Naming is mixed: chat_perplexity uses a verb_noun format, while check_deprecated_code, find_apis, get_documentation, and search use verb-based phrases without a consistent pattern. All names are snake_case, providing some readability, but the verb styles vary (e.g., 'chat' vs. 'check' vs. 'find'), lacking a unified convention.
With 5 tools, the count is reasonable for a server focused on AI assistance and information retrieval. It covers key areas like conversation, code analysis, API discovery, documentation, and general search, though it might feel slightly thin for broader AI tasks, but each tool earns its place.
The server targets AI-driven information and code assistance, with tools for chat, code checks, API finding, documentation, and search. Notable gaps include lack of update/delete operations for chats or saved searches, and no tool for summarizing or analyzing search results, which could limit agent workflows in this domain.