Skip to main content
Glama
ayoubzeroual

Perplexity MCP Server

by ayoubzeroual
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

perplexity-mcp MCP server

A Model Context Protocol (MCP) server that provides web search functionality using Perplexity AI's API. Works with the Anthropic Claude desktop client.

Example

Let's you use prompts like, "Search the web to find out what's new at Anthropic in the past week."

Related MCP server: Perplexity Web Search MCP Server

Glama Scores

Components

Prompts

The server provides a single prompt:

  • perplexity_search_web: Search the web using Perplexity AI

    • Required "query" argument for the search query

    • Optional "recency" argument to filter results by time period:

      • 'day': last 24 hours

      • 'week': last 7 days

      • 'month': last 30 days (default)

      • 'year': last 365 days

    • Uses Perplexity's API to perform web searches

Tools

The server implements one tool:

  • perplexity_search_web: Search the web using Perplexity AI

    • Takes "query" as a required string argument

    • Optional "recency" parameter to filter results (day/week/month/year)

    • Returns search results from Perplexity's API

Installation

Installing via Smithery

To install Perplexity MCP for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install perplexity-mcp --client claude

Requires UV (Fast Python package and project manager)

If uv isn't installed.

# Using Homebrew on macOS
brew install uv

or

# On macOS and Linux.
curl -LsSf https://astral.sh/uv/install.sh | sh

# On Windows.
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Environment Variables

The following environment variable is required in your claude_desktop_config.json. You can obtain an API key from Perplexity

  • PERPLEXITY_API_KEY: Your Perplexity AI API key

Optional environment variables:

  • PERPLEXITY_MODEL: The Perplexity model to use (defaults to "sonar" if not specified)

    Available models:

    • sonar-deep-research: 128k context - Enhanced research capabilities

    • sonar-reasoning-pro: 128k context - Advanced reasoning with professional focus

    • sonar-reasoning: 128k context - Enhanced reasoning capabilities

    • sonar-pro: 200k context - Professional grade model

    • sonar: 128k context - Default model

    • r1-1776: 128k context - Alternative architecture

And updated list of models is avaiable (here)[https://docs.perplexity.ai/guides/model-cards]

Cursor & Claude Desktop Installation

Add this tool as a mcp server by editing the Cursor/Claude config file.

  "perplexity-mcp": {
    "env": {
      "PERPLEXITY_API_KEY": "XXXXXXXXXXXXXXXXXXXX",
      "PERPLEXITY_MODEL": "sonar"
    },
    "command": "uvx",
    "args": [
      "perplexity-mcp"
    ]
  }

Cursor

  • On MacOS: /Users/your-username/.cursor/mcp.json

  • On Windows: C:\Users\your-username\.cursor\mcp.json

If everything is working correctly, you should now be able to call the tool from Cursor.

Claude Desktop

  • On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json

  • On Windows: %APPDATA%/Claude/claude_desktop_config.json

To verify the server is working. Open the Claude client and use a prompt like "search the web for news about openai in the past week". You should see an alert box open to confirm tool usage. Click "Allow for this chat".

Available Tools

1 tool
perplexity_search_webC

Search the web using Perplexity AI with recency filtering

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes
recencyNomonth

TDQS

C2.8/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'recency filtering' but does not explain how this affects results, potential rate limits, authentication needs, error handling, or the nature of the search output (e.g., format, pagination). This leaves significant gaps in understanding the tool's behavior beyond basic functionality.

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

Conciseness5/5

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

The description is a single, efficient sentence that front-loads the core functionality ('Search the web using Perplexity AI') and includes a key feature ('with recency filtering') without any wasted words. It is appropriately sized for the tool's complexity.

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

Completeness2/5

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

Given the complexity (a web search tool with filtering), no annotations, no output schema, and low schema description coverage, the description is incomplete. It lacks details on behavioral traits, parameter usage, result format, and error conditions, making it inadequate for the agent to fully understand how to invoke and interpret the tool effectively.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for undocumented parameters. It mentions 'recency filtering', which relates to the 'recency' parameter with enum values, adding some meaning. However, it does not explain the 'query' parameter or provide details on how recency filtering works (e.g., what 'day', 'week', etc., mean in practice), failing to fully compensate for the low coverage.

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 action ('Search the web') and the resource/context ('using Perplexity AI'), with the specific feature of 'recency filtering'. It provides a specific verb+resource combination that defines what the tool does. However, with no sibling tools mentioned, there's no opportunity to distinguish from alternatives, 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 mentions 'recency filtering' as a feature, which implies usage for time-sensitive searches, but it does not provide explicit guidance on when to use this tool versus alternatives (e.g., other search tools or methods). There are no stated exclusions, prerequisites, or comparisons, leaving the agent with minimal contextual direction.

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. 1 tool update
    • First observedperplexity_search_web
Install Server
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

B3/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear and distinct purpose focused on web search with Perplexity AI.

Naming Consistency5/5

The single tool name 'perplexity_search_web' follows a consistent verb_noun pattern. Since there is only one tool, naming consistency is inherently perfect with no deviations to assess.

Tool Count2/5

A single tool is too few for a server named 'Perplexity MCP Server', which implies broader functionality. While the tool is well-defined, the server scope feels thin and limited, lacking additional operations like query refinement or result analysis that might be expected.

Completeness2/5

The server is severely incomplete for a web search domain. It only offers a basic search tool without any supporting operations such as filtering results, getting details, or managing search history, which are common in search interfaces and would be needed for agent workflows.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

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

Related MCP Connectors

Related MCP Servers

  • Perplexity MCP Server

    jaacob
    A
    license
    B
    quality
    D
    maintenance
    Facilitates web search capabilities using Perplexity's API, allowing users to retrieve search results through Claude's interface.
    1
    7
    MIT

  • perplexity-mcp

    fastmcp-me
    A
    license
    B
    quality
    D
    maintenance
    Enables web search using Perplexity AI's API, allowing users to search the web with optional recency filters and integration with Claude, Cursor, and other MCP clients.
    1
    MIT

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ayoubzeroual/perplexity-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server