Which integrations are available for this server?

Enables web searches with Reddit-focused results through Perplexica's search API, allowing queries to be specifically targeted at Reddit content. Provides access to Wolfram Alpha computational knowledge through Perplexica's search API, enabling computational and factual queries. Enables YouTube-focused searches through Perplexica's search API, allowing queries to find and surface video content from YouTube.

How do I use MCP Perplexica?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Perplexica search for recent advancements in quantum computing with academic focus" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

MCP Perplexica

by Kaiohz

Overview Schema Source Related Servers Score Discussions Admin

Python

Hybrid

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)

Related MCP server: Perplexica MCP Server

Prerequisites

Python 3.11+
UV package manager
Running Perplexica instance

Installation

Clone the repository:

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

Install dependencies with UV:

uv sync

Create your environment file:

cp .env.example .env

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 src/main.py

SSE mode

TRANSPORT=sse PORT=8000 uv run python src/main.py

Streamable HTTP mode

TRANSPORT=streamable-http PORT=8000 uv run python src/main.py

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

Available Tools

1 tool

searchC

Search the web using Perplexica and get AI-generated responses with sources.

Args:
    search_request: The search request containing query, models, and options.

Returns:
    A formatted string containing the AI-generated response and source citations.

ParametersJSON Schema

Name	Required	Description	Default
`search_request`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must fully disclose behavioral traits. It mentions AI-generated responses and sources, but omits critical details like rate limits, authentication needs, error handling, or whether it's read-only or mutative. This leaves significant gaps in understanding the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: a clear purpose statement followed by brief sections for Args and Returns. Each sentence adds value without redundancy. However, it could be more front-loaded with key details, slightly reducing efficiency.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (AI-powered search with multiple nested parameters) and no annotations, the description is incomplete. It covers the basic purpose and return format, but lacks behavioral context, parameter details, and usage guidelines. The output schema exists, so return values are documented elsewhere, but overall gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds minimal parameter semantics: it names 'search_request' and vaguely describes it as 'containing query, models, and options.' With 0% schema description coverage, the schema lacks descriptions for properties, but the description doesn't compensate by explaining parameter meanings, formats, or constraints. The baseline is 3 since it provides some high-level context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Search the web using Perplexica and get AI-generated responses with sources.' It specifies the action (search), the resource (web via Perplexica), and the outcome (AI-generated responses with sources). However, without sibling tools, differentiation cannot be assessed, preventing a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It lacks context about prerequisites, limitations, or typical use cases. With no sibling tools, this is less critical, but it still fails to offer any usage instructions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

1 tool updateDec 28, 2025
- First observedsearch

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

B3.1/5.0

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'search' has a single, clearly defined purpose.

Naming Consistency5/5

A single tool inherently has perfect naming consistency, as there are no other tools to compare it against for patterns or conventions.

Tool Count2/5

One tool is too few for a server named 'MCP Perplexica' that implies web search functionality, as it lacks basic operations like refining searches, managing history, or handling different search modes. This feels thin and incomplete for the domain.

Completeness2/5

The tool set is severely incomplete for a web search server. It only provides a single search function with no support for advanced features like filtering, pagination, or handling different types of queries, which are essential for effective search workflows.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

Related Servers

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Looking for Admin?

If you are the server author, to access and configure the admin panel.

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MCP Perplexica

Features

Prerequisites

Installation

Usage

Transport Modes

Running with Docker Compose

Running the MCP Server (without Docker)

Stdio mode (default)

SSE mode

Streamable HTTP mode

Claude Desktop Configuration

Claude Code Configuration

Available Tools

search

Development

Install dev dependencies

Run tests

Run linter

Architecture

License

Available Tools

Output Schema

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Perplexica MCP Server

Perplexica MCP Server

Perplexity MCP Server

Perplexity Web-Search MCP

Appeared in Searches

Latest Blog Posts

MCP directory API

`search`