Perplexica MCP Server

A Model Context Protocol (MCP) server that provides search functionality using Perplexica's AI-powered search engine.

Features

Search Tool: AI-powered search with multiple source types (web, academic, discussions)
Multiple Transport Support: stdio, SSE, and Streamable HTTP transports
FastMCP Integration: Built using FastMCP for robust MCP protocol compliance
Unified Architecture: Single server implementation supporting all transport modes
Production Ready: Docker support with security best practices

Development Environment

For Claude Code Users

Important: If you are using Claude Code for development, this project requires the use of the container-use MCP server for all development operations. All file operations, code changes, and shell commands must be executed within container-use environments.

Working with Container-Use (Claude Code Only)

When contributing to this project using Claude Code, you must:

Use Container-Use Only: All file operations, code editing, and shell commands must be performed using container-use environments
View Your Work: After making changes, inform others how to access your work:
- Use container-use log <env_id> to view the development log
- Use container-use checkout <env_id> to check out your environment
No Local Operations: Do not perform file operations directly on the local filesystem

Example Development Workflow (Claude Code)

# Create a new environment for your work
container-use create --title "Your feature description"

# Make your changes using container-use tools
# (All file operations handled by container-use)

# Share your work with others
container-use log <your-env-id>
container-use checkout <your-env-id>

This ensures consistency, reproducibility, and proper version control for all development activities when using Claude Code.

For Other Development Environments

If you are not using Claude Code, you can develop normally using your preferred tools and IDE. The container-use requirement does not apply to regular development workflows.

Installation

From PyPI (Recommended)

# Install directly from PyPI
pip install perplexica-mcp

# Or using uvx for isolated execution
uvx perplexica-mcp --help

From Source

# Clone the repository
git clone https://github.com/thetom42/perplexica-mcp.git
cd perplexica-mcp

# Install dependencies
uv sync

MCP Client Configuration

To use this server with MCP clients, you need to configure the client to connect to the Perplexica MCP server. Below are configuration examples for popular MCP clients.

Important: All transport modes require proper environment variable configuration, especially:

PERPLEXICA_BACKEND_URL: URL to your Perplexica backend API

PERPLEXICA_CHAT_MODEL_PROVIDER and PERPLEXICA_CHAT_MODEL_NAME: Chat model configuration

PERPLEXICA_EMBEDDING_MODEL_PROVIDER and PERPLEXICA_EMBEDDING_MODEL_NAME: Embedding model configuration

These variables must be set either in your environment or provided in the MCP client configuration.

Claude Desktop

Stdio Transport (Recommended)

Add the following to your Claude Desktop configuration file:

Location: ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows)

{
  "mcpServers": {
    "perplexica": {
      "command": "uvx",
      "args": ["perplexica-mcp", "stdio"],
      "env": {
        "PERPLEXICA_BACKEND_URL": "http://localhost:3000/api/search",
        "PERPLEXICA_CHAT_MODEL_PROVIDER": "openai",
        "PERPLEXICA_CHAT_MODEL_NAME": "gpt-4o-mini",
        "PERPLEXICA_EMBEDDING_MODEL_PROVIDER": "openai",
        "PERPLEXICA_EMBEDDING_MODEL_NAME": "text-embedding-3-small"
      }
    }
  }
}

Alternative (from source):

{
  "mcpServers": {
    "perplexica": {
      "command": "uv",
      "args": ["run", "python", "-m", "perplexica_mcp", "stdio"],
      "cwd": "/path/to/perplexica-mcp",
      "env": {
        "PERPLEXICA_BACKEND_URL": "http://localhost:3000/api/search",
        "PERPLEXICA_CHAT_MODEL_PROVIDER": "openai",
        "PERPLEXICA_CHAT_MODEL_NAME": "gpt-4o-mini",
        "PERPLEXICA_EMBEDDING_MODEL_PROVIDER": "openai",
        "PERPLEXICA_EMBEDDING_MODEL_NAME": "text-embedding-3-small"
      }
    }
  }
}

Note: When running from source, ensure all required environment variables are set. The stdio transport requires proper model provider and model name configuration to communicate with the Perplexica backend.


#### SSE Transport

For SSE transport, first start the server:

```bash
uv run src/perplexica_mcp/server.py sse

Then configure Claude Desktop:

{
  "mcpServers": {
    "perplexica": {
      "url": "http://localhost:3001/sse"
    }
  }
}

Cursor IDE

Add to your Cursor MCP configuration:

{
  "servers": {
    "perplexica": {
      "command": "uvx",
      "args": ["perplexica-mcp", "stdio"],
      "env": {
        "PERPLEXICA_BACKEND_URL": "http://localhost:3000/api/search",
        "PERPLEXICA_CHAT_MODEL_PROVIDER": "openai",
        "PERPLEXICA_CHAT_MODEL_NAME": "gpt-4o-mini",
        "PERPLEXICA_EMBEDDING_MODEL_PROVIDER": "openai",
        "PERPLEXICA_EMBEDDING_MODEL_NAME": "text-embedding-3-small"
      }
    }
  }
}

Alternative (from source):

{
  "servers": {
    "perplexica": {
      "command": "uv",
      "args": ["run", "python", "-m", "perplexica_mcp", "stdio"],
      "cwd": "/path/to/perplexica-mcp",
      "env": {
        "PERPLEXICA_BACKEND_URL": "http://localhost:3000/api/search",
        "PERPLEXICA_CHAT_MODEL_PROVIDER": "openai",
        "PERPLEXICA_CHAT_MODEL_NAME": "gpt-4o-mini",
        "PERPLEXICA_EMBEDDING_MODEL_PROVIDER": "openai",
        "PERPLEXICA_EMBEDDING_MODEL_NAME": "text-embedding-3-small"
      }
    }
  }
}

VS Code (with MCP Extension)

Add to your VS Code MCP configuration file (.vscode/mcp.json):

{
  "servers": {
    "perplexica": {
      "type": "stdio",
      "command": "uv",
      "args": ["run", "python", "-m", "perplexica_mcp", "stdio"],
      "cwd": "/path/to/perplexica-mcp",
      "env": {
        "PERPLEXICA_BACKEND_URL": "http://localhost:3000/api/search",
        "PERPLEXICA_CHAT_MODEL_PROVIDER": "openai",
        "PERPLEXICA_CHAT_MODEL_NAME": "gpt-4o-mini",
        "PERPLEXICA_EMBEDDING_MODEL_PROVIDER": "openai",
        "PERPLEXICA_EMBEDDING_MODEL_NAME": "text-embedding-3-small"
      }
    }
  }
}

Generic MCP Client Configuration

For any MCP client supporting stdio transport:

# Command to run the server (PyPI installation)
uvx perplexica-mcp stdio

# Command to run the server with .env file (PyPI installation)
uvx --env-file .env perplexica-mcp stdio

# Command to run the server (from source)
uv run python -m perplexica_mcp stdio

# Environment variables (can be exported or set inline)
export PERPLEXICA_BACKEND_URL=http://localhost:3000/api/search
export PERPLEXICA_CHAT_MODEL_PROVIDER=openai
export PERPLEXICA_CHAT_MODEL_NAME=gpt-4o-mini
export PERPLEXICA_EMBEDDING_MODEL_PROVIDER=openai
export PERPLEXICA_EMBEDDING_MODEL_NAME=text-embedding-3-small

# Or set inline for single execution (all required vars)
PERPLEXICA_BACKEND_URL=http://localhost:3000/api/search \
PERPLEXICA_CHAT_MODEL_PROVIDER=openai \
PERPLEXICA_CHAT_MODEL_NAME=gpt-4o-mini \
PERPLEXICA_EMBEDDING_MODEL_PROVIDER=openai \
PERPLEXICA_EMBEDDING_MODEL_NAME=text-embedding-3-small \
uvx perplexica-mcp stdio

For HTTP/SSE transport clients:

# Start the server (PyPI installation)
uvx perplexica-mcp sse  # or 'http'

# Start the server (from source)
uv run /path/to/perplexica-mcp/src/perplexica_mcp/server.py sse  # or 'http'

# Connect to endpoints
SSE: http://localhost:3001/sse
HTTP: http://localhost:3002/mcp/

Configuration Notes

Path Configuration: Replace /path/to/perplexica-mcp/ with the actual path to your installation
Perplexica URL: Ensure PERPLEXICA_BACKEND_URL points to your running Perplexica instance
Transport Selection:
- Use stdio for most MCP clients (Claude Desktop, Cursor)
- Use SSE for web-based clients or real-time applications
- Use HTTP for REST API integrations
Dependencies: Ensure uvx is installed and available in your PATH (or uv for source installations)

Troubleshooting

Server not starting: Check that uvx (or uv for source) is installed and the path is correct
Connection refused: Verify Perplexica is running and accessible at the configured URL
Permission errors: Ensure the MCP client has permission to execute the server command
Environment variables: Check that PERPLEXICA_BACKEND_URL is properly set

Server Configuration

Create a .env file in the project root with your Perplexica configuration:

# Perplexica Backend Configuration
PERPLEXICA_BACKEND_URL=http://localhost:3000/api/search

# Default Model Configuration (Optional)
# If set, these models will be used as defaults when no model is specified in the search request

# Chat Model Configuration
PERPLEXICA_CHAT_MODEL_PROVIDER=openai
PERPLEXICA_CHAT_MODEL_NAME=gpt-4o-mini

# Embedding Model Configuration  
PERPLEXICA_EMBEDDING_MODEL_PROVIDER=openai
PERPLEXICA_EMBEDDING_MODEL_NAME=text-embedding-3-small

Environment Variables

Variable	Description	Default	Example
`PERPLEXICA_BACKEND_URL`	URL to Perplexica search API	`http://localhost:3000/api/search`	`http://localhost:3000/api/search`
`PERPLEXICA_CHAT_MODEL_PROVIDER`	Default chat model provider	None	`openai`, `ollama`, `anthropic`
`PERPLEXICA_CHAT_MODEL_NAME`	Default chat model name	None	`gpt-4o-mini`, `claude-3-sonnet`
`PERPLEXICA_EMBEDDING_MODEL_PROVIDER`	Default embedding model provider	None	`openai`, `ollama`
`PERPLEXICA_EMBEDDING_MODEL_NAME`	Default embedding model name	None	`text-embedding-3-small`

Note: The model environment variables are optional. If not set, you'll need to specify models in each search request. When set, they provide convenient defaults that can still be overridden per request.

Usage

The server supports three transport modes:

1. Stdio Transport

# PyPI installation
uvx perplexica-mcp stdio

# From source
uv run src/perplexica_mcp/server.py stdio

2. SSE Transport

# PyPI installation
uvx perplexica-mcp sse [host] [port]

# From source
uv run src/perplexica_mcp/server.py sse [host] [port]
# Default: localhost:3001, endpoint: /sse

3. Streamable HTTP Transport

# PyPI installation
uvx perplexica-mcp http [host] [port]

# From source
uv run src/perplexica_mcp/server.py http [host] [port]
# Default: localhost:3002, endpoint: /mcp

Docker Deployment

The server includes Docker support with multiple transport configurations for containerized deployments.

Prerequisites

Docker and Docker Compose installed
External Docker network named backend (for integration with Perplexica)

Create External Network

docker network create backend

Build and Run

Option 1: HTTP Transport (Streamable HTTP)

# Build and run with HTTP transport
docker-compose up -d

# Or build first, then run
docker-compose build
docker-compose up -d

Option 2: SSE Transport (Server-Sent Events)

# Build and run with SSE transport
docker-compose -f docker-compose-sse.yml up -d

# Or build first, then run
docker-compose -f docker-compose-sse.yml build
docker-compose -f docker-compose-sse.yml up -d

Environment Configuration

Both Docker configurations support environment variables:

# Create .env file for Docker
cat > .env << EOF
PERPLEXICA_BACKEND_URL=http://perplexica-app:3000/api/search
EOF

# Uncomment env_file in docker-compose.yml to use .env file

Or set environment variables directly in the compose file:

environment:
  - PERPLEXICA_BACKEND_URL=http://your-perplexica-host:3000/api/search

Container Details

Transport	Container Name	Port	Endpoint	Health Check
HTTP	`perplexica-mcp-http`	3001	`/mcp/`	MCP initialize request
SSE	`perplexica-mcp-sse`	3001	`/sse`	SSE endpoint check

Health Monitoring

Both containers include health checks:

# Check container health
docker ps
docker-compose ps

# View health check logs
docker logs perplexica-mcp-http
docker logs perplexica-mcp-sse

Integration with Perplexica

The Docker setup assumes Perplexica is running in the same Docker network:

# Example Perplexica service in the same compose file
services:
  perplexica-app:
    # ... your Perplexica configuration
    networks:
      - backend
  
  perplexica-mcp:
    # ... MCP server configuration
    environment:
      - PERPLEXICA_BACKEND_URL=http://perplexica-app:3000/api/search
    networks:
      - backend

Production Considerations

Both containers use restart: unless-stopped for reliability
Health checks ensure service availability
External network allows integration with existing Perplexica deployments
Security best practices implemented in Dockerfile

Available Tools

search

Performs AI-powered web search using Perplexica.

Parameters:

query (string, required): Search query
sources (array, required): Search sources to use. Valid values: "web", "academic", "discussions". Can combine multiple sources.
chat_model (object, optional): Chat model configuration
embedding_model (object, optional): Embedding model configuration
optimization_mode (string, optional): "speed", "balanced", or "quality"
history (array, optional): Conversation history as [["human", "text"], ["assistant", "text"]] pairs
system_instructions (string, optional): Custom instructions
stream (boolean, optional): Whether to stream responses

Testing

Run the comprehensive test suite to verify all transports:

uv run src/test_transports.py

This will test:

✓ Stdio transport with MCP protocol handshake
✓ HTTP transport with Streamable HTTP compliance
✓ SSE transport endpoint accessibility

Transport Details

Stdio Transport

Uses FastMCP's built-in stdio server
Supports full MCP protocol including initialization and tool listing
Ideal for MCP client integration

SSE Transport

Server-Sent Events for real-time communication
Endpoint: http://host:port/sse
Includes periodic ping messages for connection health

Streamable HTTP Transport

Compliant with MCP Streamable HTTP specification
Endpoint: http://host:port/mcp
Returns 307 redirect to /mcp/ as per protocol
Uses StreamableHTTPSessionManager for proper session handling

Development

The server is built using:

FastMCP: Modern MCP server framework with built-in transport support
Uvicorn: ASGI server for SSE and HTTP transports
httpx: HTTP client for Perplexica API communication
python-dotenv: Environment variable management

Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   MCP Client    │◄──►│ Perplexica MCP   │◄──►│   Perplexica    │
│                 │    │     Server       │    │   Search API    │
│  (stdio/SSE/    │    │   (FastMCP)      │    │                 │
│   HTTP)         │    │                  │    │                 │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                              │
                              ▼
                       ┌──────────────┐
                       │   FastMCP    │
                       │  Framework   │
                       │ ┌──────────┐ │
                       │ │  stdio   │ │
                       │ │   SSE    │ │
                       │ │  HTTP    │ │
                       │ └──────────┘ │
                       └──────────────┘

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Fork the repository
Create a feature branch (using container-use environments if using Claude Code)
Make your changes (within container-use environment if using Claude Code)
Add tests if applicable
Submit a pull request
If using Claude Code, provide access to your work via container-use log <env_id> and container-use checkout <env_id>

Support

For issues and questions:

Check the troubleshooting section
Review the Perplexica documentation
Open an issue on GitHub

Perplexica MCP Server

A Model Context Protocol (MCP) server that provides search functionality using Perplexica's AI-powered search engine.

Features

Search Tool: AI-powered search with multiple source types (web, academic, discussions)
Multiple Transport Support: stdio, SSE, and Streamable HTTP transports
FastMCP Integration: Built using FastMCP for robust MCP protocol compliance
Unified Architecture: Single server implementation supporting all transport modes
Production Ready: Docker support with security best practices

Development Environment

For Claude Code Users

Working with Container-Use (Claude Code Only)

When contributing to this project using Claude Code, you must:

Use Container-Use Only: All file operations, code editing, and shell commands must be performed using container-use environments
View Your Work: After making changes, inform others how to access your work:
- Use container-use log <env_id> to view the development log
- Use container-use checkout <env_id> to check out your environment
No Local Operations: Do not perform file operations directly on the local filesystem

Example Development Workflow (Claude Code)

# Create a new environment for your work
container-use create --title "Your feature description"

# Make your changes using container-use tools
# (All file operations handled by container-use)

# Share your work with others
container-use log <your-env-id>
container-use checkout <your-env-id>

This ensures consistency, reproducibility, and proper version control for all development activities when using Claude Code.

For Other Development Environments

If you are not using Claude Code, you can develop normally using your preferred tools and IDE. The container-use requirement does not apply to regular development workflows.

Installation

From PyPI (Recommended)

# Install directly from PyPI
pip install perplexica-mcp

# Or using uvx for isolated execution
uvx perplexica-mcp --help

From Source

# Clone the repository
git clone https://github.com/thetom42/perplexica-mcp.git
cd perplexica-mcp

# Install dependencies
uv sync

MCP Client Configuration

To use this server with MCP clients, you need to configure the client to connect to the Perplexica MCP server. Below are configuration examples for popular MCP clients.

Important: All transport modes require proper environment variable configuration, especially:

PERPLEXICA_BACKEND_URL: URL to your Perplexica backend API

PERPLEXICA_CHAT_MODEL_PROVIDER and PERPLEXICA_CHAT_MODEL_NAME: Chat model configuration

PERPLEXICA_EMBEDDING_MODEL_PROVIDER and PERPLEXICA_EMBEDDING_MODEL_NAME: Embedding model configuration

These variables must be set either in your environment or provided in the MCP client configuration.

Claude Desktop

Stdio Transport (Recommended)

Add the following to your Claude Desktop configuration file:

Location: ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows)

{
  "mcpServers": {
    "perplexica": {
      "command": "uvx",
      "args": ["perplexica-mcp", "stdio"],
      "env": {
        "PERPLEXICA_BACKEND_URL": "http://localhost:3000/api/search",
        "PERPLEXICA_CHAT_MODEL_PROVIDER": "openai",
        "PERPLEXICA_CHAT_MODEL_NAME": "gpt-4o-mini",
        "PERPLEXICA_EMBEDDING_MODEL_PROVIDER": "openai",
        "PERPLEXICA_EMBEDDING_MODEL_NAME": "text-embedding-3-small"
      }
    }
  }
}

Alternative (from source):

{
  "mcpServers": {
    "perplexica": {
      "command": "uv",
      "args": ["run", "python", "-m", "perplexica_mcp", "stdio"],
      "cwd": "/path/to/perplexica-mcp",
      "env": {
        "PERPLEXICA_BACKEND_URL": "http://localhost:3000/api/search",
        "PERPLEXICA_CHAT_MODEL_PROVIDER": "openai",
        "PERPLEXICA_CHAT_MODEL_NAME": "gpt-4o-mini",
        "PERPLEXICA_EMBEDDING_MODEL_PROVIDER": "openai",
        "PERPLEXICA_EMBEDDING_MODEL_NAME": "text-embedding-3-small"
      }
    }
  }
}

Note: When running from source, ensure all required environment variables are set. The stdio transport requires proper model provider and model name configuration to communicate with the Perplexica backend.


#### SSE Transport

For SSE transport, first start the server:

```bash
uv run src/perplexica_mcp/server.py sse

Then configure Claude Desktop:

{
  "mcpServers": {
    "perplexica": {
      "url": "http://localhost:3001/sse"
    }
  }
}

Cursor IDE

Add to your Cursor MCP configuration:

{
  "servers": {
    "perplexica": {
      "command": "uvx",
      "args": ["perplexica-mcp", "stdio"],
      "env": {
        "PERPLEXICA_BACKEND_URL": "http://localhost:3000/api/search",
        "PERPLEXICA_CHAT_MODEL_PROVIDER": "openai",
        "PERPLEXICA_CHAT_MODEL_NAME": "gpt-4o-mini",
        "PERPLEXICA_EMBEDDING_MODEL_PROVIDER": "openai",
        "PERPLEXICA_EMBEDDING_MODEL_NAME": "text-embedding-3-small"
      }
    }
  }
}

Alternative (from source):

{
  "servers": {
    "perplexica": {
      "command": "uv",
      "args": ["run", "python", "-m", "perplexica_mcp", "stdio"],
      "cwd": "/path/to/perplexica-mcp",
      "env": {
        "PERPLEXICA_BACKEND_URL": "http://localhost:3000/api/search",
        "PERPLEXICA_CHAT_MODEL_PROVIDER": "openai",
        "PERPLEXICA_CHAT_MODEL_NAME": "gpt-4o-mini",
        "PERPLEXICA_EMBEDDING_MODEL_PROVIDER": "openai",
        "PERPLEXICA_EMBEDDING_MODEL_NAME": "text-embedding-3-small"
      }
    }
  }
}

VS Code (with MCP Extension)

Add to your VS Code MCP configuration file (.vscode/mcp.json):

{
  "servers": {
    "perplexica": {
      "type": "stdio",
      "command": "uv",
      "args": ["run", "python", "-m", "perplexica_mcp", "stdio"],
      "cwd": "/path/to/perplexica-mcp",
      "env": {
        "PERPLEXICA_BACKEND_URL": "http://localhost:3000/api/search",
        "PERPLEXICA_CHAT_MODEL_PROVIDER": "openai",
        "PERPLEXICA_CHAT_MODEL_NAME": "gpt-4o-mini",
        "PERPLEXICA_EMBEDDING_MODEL_PROVIDER": "openai",
        "PERPLEXICA_EMBEDDING_MODEL_NAME": "text-embedding-3-small"
      }
    }
  }
}

Generic MCP Client Configuration

For any MCP client supporting stdio transport:

# Command to run the server (PyPI installation)
uvx perplexica-mcp stdio

# Command to run the server with .env file (PyPI installation)
uvx --env-file .env perplexica-mcp stdio

# Command to run the server (from source)
uv run python -m perplexica_mcp stdio

# Environment variables (can be exported or set inline)
export PERPLEXICA_BACKEND_URL=http://localhost:3000/api/search
export PERPLEXICA_CHAT_MODEL_PROVIDER=openai
export PERPLEXICA_CHAT_MODEL_NAME=gpt-4o-mini
export PERPLEXICA_EMBEDDING_MODEL_PROVIDER=openai
export PERPLEXICA_EMBEDDING_MODEL_NAME=text-embedding-3-small

# Or set inline for single execution (all required vars)
PERPLEXICA_BACKEND_URL=http://localhost:3000/api/search \
PERPLEXICA_CHAT_MODEL_PROVIDER=openai \
PERPLEXICA_CHAT_MODEL_NAME=gpt-4o-mini \
PERPLEXICA_EMBEDDING_MODEL_PROVIDER=openai \
PERPLEXICA_EMBEDDING_MODEL_NAME=text-embedding-3-small \
uvx perplexica-mcp stdio

For HTTP/SSE transport clients:

# Start the server (PyPI installation)
uvx perplexica-mcp sse  # or 'http'

# Start the server (from source)
uv run /path/to/perplexica-mcp/src/perplexica_mcp/server.py sse  # or 'http'

# Connect to endpoints
SSE: http://localhost:3001/sse
HTTP: http://localhost:3002/mcp/

Configuration Notes

Path Configuration: Replace /path/to/perplexica-mcp/ with the actual path to your installation
Perplexica URL: Ensure PERPLEXICA_BACKEND_URL points to your running Perplexica instance
Transport Selection:
- Use stdio for most MCP clients (Claude Desktop, Cursor)
- Use SSE for web-based clients or real-time applications
- Use HTTP for REST API integrations
Dependencies: Ensure uvx is installed and available in your PATH (or uv for source installations)

Troubleshooting

Server not starting: Check that uvx (or uv for source) is installed and the path is correct
Connection refused: Verify Perplexica is running and accessible at the configured URL
Permission errors: Ensure the MCP client has permission to execute the server command
Environment variables: Check that PERPLEXICA_BACKEND_URL is properly set

Server Configuration

Create a .env file in the project root with your Perplexica configuration:

# Perplexica Backend Configuration
PERPLEXICA_BACKEND_URL=http://localhost:3000/api/search

# Default Model Configuration (Optional)
# If set, these models will be used as defaults when no model is specified in the search request

# Chat Model Configuration
PERPLEXICA_CHAT_MODEL_PROVIDER=openai
PERPLEXICA_CHAT_MODEL_NAME=gpt-4o-mini

# Embedding Model Configuration  
PERPLEXICA_EMBEDDING_MODEL_PROVIDER=openai
PERPLEXICA_EMBEDDING_MODEL_NAME=text-embedding-3-small

Environment Variables

Variable	Description	Default	Example
`PERPLEXICA_BACKEND_URL`	URL to Perplexica search API	`http://localhost:3000/api/search`	`http://localhost:3000/api/search`
`PERPLEXICA_CHAT_MODEL_PROVIDER`	Default chat model provider	None	`openai`, `ollama`, `anthropic`
`PERPLEXICA_CHAT_MODEL_NAME`	Default chat model name	None	`gpt-4o-mini`, `claude-3-sonnet`
`PERPLEXICA_EMBEDDING_MODEL_PROVIDER`	Default embedding model provider	None	`openai`, `ollama`
`PERPLEXICA_EMBEDDING_MODEL_NAME`	Default embedding model name	None	`text-embedding-3-small`

Usage

The server supports three transport modes:

1. Stdio Transport

# PyPI installation
uvx perplexica-mcp stdio

# From source
uv run src/perplexica_mcp/server.py stdio

2. SSE Transport

# PyPI installation
uvx perplexica-mcp sse [host] [port]

# From source
uv run src/perplexica_mcp/server.py sse [host] [port]
# Default: localhost:3001, endpoint: /sse

3. Streamable HTTP Transport

# PyPI installation
uvx perplexica-mcp http [host] [port]

# From source
uv run src/perplexica_mcp/server.py http [host] [port]
# Default: localhost:3002, endpoint: /mcp

Docker Deployment

The server includes Docker support with multiple transport configurations for containerized deployments.

Prerequisites

Docker and Docker Compose installed
External Docker network named backend (for integration with Perplexica)

Create External Network

docker network create backend

Build and Run

Option 1: HTTP Transport (Streamable HTTP)

# Build and run with HTTP transport
docker-compose up -d

# Or build first, then run
docker-compose build
docker-compose up -d

Option 2: SSE Transport (Server-Sent Events)

# Build and run with SSE transport
docker-compose -f docker-compose-sse.yml up -d

# Or build first, then run
docker-compose -f docker-compose-sse.yml build
docker-compose -f docker-compose-sse.yml up -d

Environment Configuration

Both Docker configurations support environment variables:

# Create .env file for Docker
cat > .env << EOF
PERPLEXICA_BACKEND_URL=http://perplexica-app:3000/api/search
EOF

# Uncomment env_file in docker-compose.yml to use .env file

Or set environment variables directly in the compose file:

environment:
  - PERPLEXICA_BACKEND_URL=http://your-perplexica-host:3000/api/search

Container Details

Transport	Container Name	Port	Endpoint	Health Check
HTTP	`perplexica-mcp-http`	3001	`/mcp/`	MCP initialize request
SSE	`perplexica-mcp-sse`	3001	`/sse`	SSE endpoint check

Health Monitoring

Both containers include health checks:

# Check container health
docker ps
docker-compose ps

# View health check logs
docker logs perplexica-mcp-http
docker logs perplexica-mcp-sse

Integration with Perplexica

The Docker setup assumes Perplexica is running in the same Docker network:

# Example Perplexica service in the same compose file
services:
  perplexica-app:
    # ... your Perplexica configuration
    networks:
      - backend
  
  perplexica-mcp:
    # ... MCP server configuration
    environment:
      - PERPLEXICA_BACKEND_URL=http://perplexica-app:3000/api/search
    networks:
      - backend

Production Considerations

Both containers use restart: unless-stopped for reliability
Health checks ensure service availability
External network allows integration with existing Perplexica deployments
Security best practices implemented in Dockerfile

Available Tools

search

Performs AI-powered web search using Perplexica.

Parameters:

query (string, required): Search query
sources (array, required): Search sources to use. Valid values: "web", "academic", "discussions". Can combine multiple sources.
chat_model (object, optional): Chat model configuration
embedding_model (object, optional): Embedding model configuration
optimization_mode (string, optional): "speed", "balanced", or "quality"
history (array, optional): Conversation history as [["human", "text"], ["assistant", "text"]] pairs
system_instructions (string, optional): Custom instructions
stream (boolean, optional): Whether to stream responses

Testing

Run the comprehensive test suite to verify all transports:

uv run src/test_transports.py

This will test:

✓ Stdio transport with MCP protocol handshake
✓ HTTP transport with Streamable HTTP compliance
✓ SSE transport endpoint accessibility

Transport Details

Stdio Transport

Uses FastMCP's built-in stdio server
Supports full MCP protocol including initialization and tool listing
Ideal for MCP client integration

SSE Transport

Server-Sent Events for real-time communication
Endpoint: http://host:port/sse
Includes periodic ping messages for connection health

Streamable HTTP Transport

Compliant with MCP Streamable HTTP specification
Endpoint: http://host:port/mcp
Returns 307 redirect to /mcp/ as per protocol
Uses StreamableHTTPSessionManager for proper session handling

Development

The server is built using:

FastMCP: Modern MCP server framework with built-in transport support
Uvicorn: ASGI server for SSE and HTTP transports
httpx: HTTP client for Perplexica API communication
python-dotenv: Environment variable management

Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   MCP Client    │◄──►│ Perplexica MCP   │◄──►│   Perplexica    │
│                 │    │     Server       │    │   Search API    │
│  (stdio/SSE/    │    │   (FastMCP)      │    │                 │
│   HTTP)         │    │                  │    │                 │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                              │
                              ▼
                       ┌──────────────┐
                       │   FastMCP    │
                       │  Framework   │
                       │ ┌──────────┐ │
                       │ │  stdio   │ │
                       │ │   SSE    │ │
                       │ │  HTTP    │ │
                       │ └──────────┘ │
                       └──────────────┘

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Fork the repository
Create a feature branch (using container-use environments if using Claude Code)
Make your changes (within container-use environment if using Claude Code)
Add tests if applicable
Submit a pull request
If using Claude Code, provide access to your work via container-use log <env_id> and container-use checkout <env_id>

Support

For issues and questions:

Check the troubleshooting section
Review the Perplexica documentation
Open an issue on GitHub