Perplexity MCP Server
Perplexity MCP サーバー
これは、Perplexity AI を使用して Web を検索できるシンプルな MCP サーバーです。
インストール
前提条件
Node.js 18+ ( nodejs.orgからダウンロード)
Git ( git-scm.comからダウンロード)
Perplexity AI APIキー
Windowsの場合の手順
リポジトリをクローンします。
git clone [repository-url]
cd perplexity-mcp-server依存関係をインストールします:
npm install環境ファイルを作成します:
ルートディレクトリに
.envという名前の新しいファイルを作成します。Perplexity AI APIキーを追加します: GXP3
Related MCP server: Tavily MCP Server
Windowsでの使用
開発モード
npm run dev生産モード
プロジェクトをビルドします。
npm run buildサーバーを起動します。
npm startWindows バッチファイルの使用(推奨)
Windows での環境変数の問題を回避するには、付属のバッチ ファイルを使用します。
まずプロジェクトをビルドします。
npm run buildバッチファイルを実行します:
.\run-perplexity-server.batまたは、Windows エクスプローラーでrun-perplexity-server.batファイルをダブルクリックします。
詳細な手順については、 Windows セットアップ ガイドを参照してください。
Windowsの問題のトラブルシューティング
よくある問題
権限エラー
コマンドプロンプトまたはPowerShellを管理者として実行する
プロジェクトディレクトリ内のファイル権限を確認する
環境変数
.envファイルがルートディレクトリにあることを確認する.envファイル内の=記号の前後にスペースを入れないでください環境変数を変更した後はターミナルを再起動してください
環境変数の問題を回避するために、提供されているバッチファイルを使用してください。
Node.js の問題
Node.jsのインストールを確認する:
node --versionnpm がインストールされていることを確認する:
npm --version
エラーメッセージ
Error: ENOENT: no such file or directoryが表示された場合は、次の点を確認してください。
すべてのパスは正しいWindowsスタイルの区切り文字を使用します
正しいディレクトリにいます
必要なファイルが存在する
プロジェクト構造
perplexity-mcp-server/
├── src/
│ └── index.ts # Main server implementation
├── dist/ # Compiled output
├── .env # Environment variables
├── package.json # Project configuration
├── run-perplexity-server.bat # Windows batch file
└── tsconfig.json # TypeScript configurationAPIの使用
サーバーは、Web 検索用の単一のツールperplexity_searchを提供します。
{
"name": "perplexity_search",
"arguments": {
"query": "your search query here"
}
}ライセンス
マサチューセッツ工科大学
Available Tools
1 toolperplexity_searchC
Search the web using Perplexity AI
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The search query |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Search the web' implies a read-only operation, it doesn't address critical aspects like rate limits, authentication requirements, response format, or whether it's a real-time search versus cached results. The description is too minimal for a tool that interacts with external services.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence with zero wasted words. It's front-loaded with the core functionality and uses straightforward language. For a simple search tool, this level of brevity is appropriate.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there are no annotations and no output schema, the description is insufficiently complete. It doesn't explain what kind of results to expect, whether there are usage limitations, or how the search differs from standard web searches. For a tool that presumably returns search results, more context about the output would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'query' clearly documented in the schema. The description doesn't add any additional semantic context about the parameter beyond what's already in the schema, so it meets the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
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/mechanism ('using Perplexity AI'), providing a specific verb+resource combination. However, with no sibling tools mentioned, there's no opportunity to distinguish from alternatives, so it cannot achieve a perfect score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 other search methods or alternatives. It simply states what the tool does without any context about appropriate use cases, prerequisites, or limitations.
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 update
- First observed
perplexity_search
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear and distinct purpose, making it impossible for an agent to misselect between multiple options.
A single tool inherently has perfect naming consistency, as there are no other tools to compare it against for patterns. The name 'perplexity_search' follows a clear verb_noun format, but consistency cannot be assessed across a set of one.
A single tool is generally too few for most server purposes, as it limits functionality and flexibility. For a web search server, this minimal scope might suffice for basic queries, but it feels thin and lacks auxiliary operations like filtering or advanced search options.
The server's purpose appears to be web search, and the single tool covers the core action of searching. However, there are notable gaps, such as no tools for refining searches, handling different search types (e.g., news, images), or managing search history, which could lead to agent workarounds or limitations.
Maintenance
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
Real-time web search, reasoning, and research through Perplexity's API
Enable AI assistants to perform web searches using Perplexity's Sonar Pro.
Provides AI assistants with access to Seltz's powerful Web Search capabilities.
Web search, scraping, Google Trends and data lookups. Paid per call in USDC on Base via x402.
Related MCP Servers
- AlicenseBqualityFmaintenanceA server facilitating web search functionality by utilizing Perplexity AI's API, designed to integrate with the Claude desktop client for enhanced search queries.1308MIT

Tavily MCP Serverofficial
AlicenseAqualityDmaintenanceThis server enables AI systems to integrate with Tavily's search and data extraction tools, providing real-time web information access and domain-specific searches.416,9762,370MIT- FlicenseBqualityNot gradedmaintenanceA server that allows AI assistants to perform web searches using Perplexity's sonar-deep-research model with citation support.111-
- AlicenseNot gradedqualityDmaintenanceA Type 3 DAuth MCP server for Perplexity AI API that provides web search, chat, embeddings, and content moderation using Perplexity's search-focused AI models.MIT
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