ai-mldeveloper-tools

Perplexity Search

by spences10

Perplexity Search is an AI writing tool using real-time web search for fast fact-checking, research, and high-quality co

Integrates with Perplexity's web search API to enable real-time fact-checking, research, and content generation using up-to-date information.

github stars

8

Repository no longer maintainedFunctionality moved to mcp-omnisearchPredefined templates for common tasks

best for

  • / Developers needing AI-powered documentation generation
  • / Code review and improvement workflows
  • / Security analysis and best practices guidance

capabilities

  • / Generate technical documentation using AI
  • / Analyze security best practices
  • / Review and improve code
  • / Create API documentation in structured formats
  • / Use custom prompt templates
  • / Configure model parameters and output formats

what it does

Integrates Perplexity's AI API to provide chat completion capabilities with predefined prompt templates for technical documentation, code review, and other specialized use cases.

about

Perplexity Search is a community-built MCP server published by spences10 that provides AI assistants with tools and capabilities via the Model Context Protocol. Perplexity Search is an AI writing tool using real-time web search for fast fact-checking, research, and high-quality co It is categorized under ai ml, developer tools.

how to install

You can install Perplexity Search in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Perplexity Search is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

mcp-perplexity-search


⚠️ Notice

This repository is no longer maintained.

The functionality of this tool is now available in mcp-omnisearch, which combines multiple MCP tools in one unified package.

Please use mcp-omnisearch instead.


A Model Context Protocol (MCP) server for integrating Perplexity's AI API with LLMs. This server provides advanced chat completion capabilities with specialized prompt templates for various use cases.

<a href="https://glama.ai/mcp/servers/zlqdizpsr9"> <img width="380" height="200" src="https://glama.ai/mcp/servers/zlqdizpsr9/badge" /> </a>

Features

  • 🤖 Advanced chat completion using Perplexity's AI models
  • 📝 Predefined prompt templates for common scenarios:
    • Technical documentation generation
    • Security best practices analysis
    • Code review and improvements
    • API documentation in structured format
  • 🎯 Custom template support for specialized use cases
  • 📊 Multiple output formats (text, markdown, JSON)
  • 🔍 Optional source URL inclusion in responses
  • ⚙️ Configurable model parameters (temperature, max tokens)
  • 🚀 Support for various Perplexity models including Sonar and LLaMA

Configuration

This server requires configuration through your MCP client. Here are examples for different environments:

Cline Configuration

Add this to your Cline MCP settings:

{
	"mcpServers": {
		"mcp-perplexity-search": {
			"command": "npx",
			"args": ["-y", "mcp-perplexity-search"],
			"env": {
				"PERPLEXITY_API_KEY": "your-perplexity-api-key"
			}
		}
	}
}

Claude Desktop with WSL Configuration

For WSL environments, add this to your Claude Desktop configuration:

{
	"mcpServers": {
		"mcp-perplexity-search": {
			"command": "wsl.exe",
			"args": [
				"bash",
				"-c",
				"source ~/.nvm/nvm.sh && PERPLEXITY_API_KEY=your-perplexity-api-key /home/username/.nvm/versions/node/v20.12.1/bin/npx mcp-perplexity-search"
			]
		}
	}
}

Environment Variables

The server requires the following environment variable:

  • PERPLEXITY_API_KEY: Your Perplexity API key (required)

API

The server implements a single MCP tool with configurable parameters:

chat_completion

Generate chat completions using the Perplexity API with support for specialized prompt templates.

Parameters:

  • messages (array, required): Array of message objects with:
    • role (string): 'system', 'user', or 'assistant'
    • content (string): The message content
  • prompt_template (string, optional): Predefined template to use:
    • technical_docs: Technical documentation with code examples
    • security_practices: Security implementation guidelines
    • code_review: Code analysis and improvements
    • api_docs: API documentation in JSON format
  • custom_template (object, optional): Custom prompt template with:
    • system (string): System message for assistant behaviour
    • format (string): Output format preference
    • include_sources (boolean): Whether to include sources
  • format (string, optional): 'text', 'markdown', or 'json' (default: 'text')
  • include_sources (boolean, optional): Include source URLs (default: false)
  • model (string, optional): Perplexity model to use (default: 'sonar')
  • temperature (number, optional): Output randomness (0-1, default: 0.7)
  • max_tokens (number, optional): Maximum response length (default: 1024)

Development

Setup

  1. Clone the repository
  2. Install dependencies:
pnpm install
  1. Build the project:
pnpm build
  1. Run in development mode:
pnpm dev

Publishing

The project uses changesets for version management. To publish:

  1. Create a changeset:
pnpm changeset
  1. Version the package:
pnpm changeset version
  1. Publish to npm:
pnpm release

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see the LICENSE file for details.

Acknowledgments

FAQ

What is the Perplexity Search MCP server?
Perplexity Search is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Perplexity Search?
This profile displays 10 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
MCP server reviews

Ratings

4.510 reviews
  • Shikha Mishra· Oct 10, 2024

    Perplexity Search is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Piyush G· Sep 9, 2024

    We evaluated Perplexity Search against two servers with overlapping tools; this profile had the clearer scope statement.

  • Chaitanya Patil· Aug 8, 2024

    Useful MCP listing: Perplexity Search is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Sakshi Patil· Jul 7, 2024

    Perplexity Search reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Ganesh Mohane· Jun 6, 2024

    I recommend Perplexity Search for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Oshnikdeep· May 5, 2024

    Strong directory entry: Perplexity Search surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Dhruvi Jain· Apr 4, 2024

    Perplexity Search has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Rahul Santra· Mar 3, 2024

    According to our notes, Perplexity Search benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Pratham Ware· Feb 2, 2024

    We wired Perplexity Search into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Yash Thakker· Jan 1, 2024

    Perplexity Search is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.