MCP Perplexica

MCP server proxy for Perplexica search API.

This server allows LLMs to perform web searches through Perplexica using the Model Context Protocol (MCP).

Features

• 🔍 Web search through Perplexica
• 📚 Multiple focus modes (web, academic, YouTube, Reddit, etc.)
• ⚡ Configurable optimization modes (speed, balanced, quality)
• 🔧 Customizable model configuration
• 📖 Source citations in responses
• 🚀 Multiple transport modes (stdio, SSE, Streamable HTTP)

Prerequisites

• Python 3.11+
• UV package manager
• Running Perplexica instance

Installation

1. Clone the repository:

git clone https://github.com/Kaiohz/mcp-perplexica.git
cd mcp-perplexica

2. Install dependencies with UV:

uv sync

3. Create your environment file:

cp .env.example .env

4. Edit .env with your configuration:

# Perplexica API
PERPLEXICA_URL=http://localhost:3000

# Transport: stdio (default), sse, or streamable-http
TRANSPORT=stdio
HOST=127.0.0.1
PORT=8000

# Model configuration
DEFAULT_CHAT_MODEL_PROVIDER_ID=your-provider-id
DEFAULT_CHAT_MODEL_KEY=anthropic/claude-sonnet-4.5
DEFAULT_EMBEDDING_MODEL_PROVIDER_ID=your-provider-id
DEFAULT_EMBEDDING_MODEL_KEY=openai/text-embedding-3-small

Usage

Transport Modes

The server supports three transport modes:

Transport	Description	Use Case
stdio	Standard input/output	CLI tools, Claude Desktop
sse	Server-Sent Events over HTTP	Web clients
streamable-http	Streamable HTTP (recommended for production)	Production deployments

Running with Docker Compose

The easiest way to run both Perplexica and MCP Perplexica together:

# Copy and configure environment files
cp .env.example .env
cp .env.perplexica.example .env.perplexica

# Edit .env with your MCP Perplexica settings
# Edit .env.perplexica with your Perplexica settings

# Start services
docker compose up -d

This starts:

• Perplexica on http://localhost:3000
• MCP Perplexica connected to Perplexica

Running the MCP Server (without Docker)

Stdio mode (default)

uv run python -m main

SSE mode

TRANSPORT=sse PORT=8000 uv run python -m main

Streamable HTTP mode

TRANSPORT=streamable-http PORT=8000 uv run python -m main

Claude Desktop Configuration

Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "perplexica": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/mcp-perplexica", "python", "-m", "main"],
      "env": {
        "PERPLEXICA_URL": "http://localhost:3000",
        "TRANSPORT": "stdio",
        "DEFAULT_CHAT_MODEL_PROVIDER_ID": "your-provider-id",
        "DEFAULT_CHAT_MODEL_KEY": "anthropic/claude-sonnet-4.5",
        "DEFAULT_EMBEDDING_MODEL_PROVIDER_ID": "your-provider-id",
        "DEFAULT_EMBEDDING_MODEL_KEY": "openai/text-embedding-3-small"
      }
    }
  }
}

Claude Code Configuration

For HTTP-based transports, you can add the server to Claude Code:

# Start the server with streamable-http transport
TRANSPORT=streamable-http PORT=8000 uv run python -m main

# Add to Claude Code
claude mcp add --transport http perplexica http://localhost:8000/mcp

Available Tools

search

Perform a web search using Perplexica.

Parameters:

Parameter	Type	Required	Description
query	string	Yes	The search query
focus_mode	string	No	Search focus: webSearch, academicSearch, writingAssistant, wolframAlphaSearch, youtubeSearch, redditSearch
optimization_mode	string	No	Optimization: speed, balanced, quality
system_instructions	string	No	Custom instructions for AI response
chat_model_provider_id	string	No	Override default chat model provider
chat_model_key	string	No	Override default chat model
embedding_model_provider_id	string	No	Override default embedding provider
embedding_model_key	string	No	Override default embedding model

Example:

Search for "latest developments in AI" using academic focus

Development

Install dev dependencies

uv sync --dev

Run tests

uv run pytest

Run linter

uv run ruff check .
uv run ruff format .
uv run black src/

Architecture

This project follows hexagonal architecture:

src/
├── main.py              # MCP server entry point
├── config.py            # Pydantic Settings
├── dependencies.py      # Dependency injection
├── domain/              # Business core (pure Python)
│   ├── entities.py      # Dataclasses
│   └── ports.py         # ABC interfaces
├── application/         # Use cases
│   ├── requests.py      # Pydantic DTOs
│   └── use_cases.py     # Business logic
└── infrastructure/      # External adapters
    └── perplexica/
        └── adapter.py   # HTTP client

License

MIT