Perplexity MCP Server
perplexity-mcp MCP サーバー
Perplexity AIのAPIを使用してウェブ検索機能を提供するModel Context Protocol(MCP)サーバー。Anthropic Claudeデスクトップクライアントと連携します。
例
「Web を検索して、過去 1 週間の Anthropic の新着情報を調べてください」のようなプロンプトを使用しましょう。
Related MCP server: Perplexity MCP Server
グラマスコア
コンポーネント
プロンプト
サーバーは単一のプロンプトを提供します:
perplexity_search_web: Perplexity AI を使用して Web を検索する
検索クエリに必要な「クエリ」引数
期間別に結果をフィルタリングするためのオプションの「recency」引数:
「日」: 過去24時間
「週」: 過去7日間
'month': 過去30日間(デフォルト)
「年」: 過去365日間
PerplexityのAPIを使用してWeb検索を実行します
ツール
サーバーは 1 つのツールを実装します。
perplexity_search_web: Perplexity AI を使用して Web を検索する
必須の文字列引数として「query」を受け取ります
結果をフィルタリングするためのオプションの「recency」パラメータ(日/週/月/年)
PerplexityのAPIから検索結果を返します
インストール
Smithery経由でインストール
Smithery経由で Claude Desktop 用の Perplexity MCP を自動的にインストールするには:
npx -y @smithery/cli install perplexity-mcp --client claudeUV (高速Pythonパッケージとプロジェクトマネージャー)が必要です
uvがインストールされていない場合。
# Using Homebrew on macOS
brew install uvまたは
# 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"環境変数
claude_desktop_config.json に以下の環境変数が必要です。APIキーはPerplexityから取得できます。
PERPLEXITY_API_KEY: Perplexity AI APIキー
オプションの環境変数:
PERPLEXITY_MODEL: 使用する Perplexity モデル (指定されていない場合はデフォルトで「sonar」になります)利用可能なモデル:
sonar-deep-research: 128k コンテキスト - 強化された調査機能sonar-reasoning-pro: 128k コンテキスト - 専門的な視点に立った高度な推論sonar-reasoning: 128kコンテキスト - 強化された推論機能sonar-pro: 200kコンテキスト - プロフェッショナルグレードモデルsonar:128kコンテキスト - デフォルトモデルr1-1776: 128kコンテキスト - 代替アーキテクチャ
更新されたモデルのリストは(こちら)[ https://docs.perplexity.ai/guides/model-cards ]でご覧いただけます。
カーソルとクロードのデスクトップインストール
Cursor/Claude 構成ファイルを編集して、このツールを mcp サーバーとして追加します。
"perplexity-mcp": {
"env": {
"PERPLEXITY_API_KEY": "XXXXXXXXXXXXXXXXXXXX",
"PERPLEXITY_MODEL": "sonar"
},
"command": "uvx",
"args": [
"perplexity-mcp"
]
}カーソル
MacOSの場合:
/Users/your-username/.cursor/mcp.jsonWindowsの場合:
C:\Users\your-username\.cursor\mcp.json
すべてが正しく動作している場合は、カーソルからツールを呼び出すことができるようになります。
クロードデスクトップ
MacOSの場合:
~/Library/Application\ Support/Claude/claude_desktop_config.jsonWindows の場合:
%APPDATA%/Claude/claude_desktop_config.json
サーバーが動作していることを確認するには、Claudeクライアントを開き、「過去1週間のOpenAIに関するニュースをWebで検索」などのプロンプトを使用します。ツールの使用を確認するための警告ボックスが表示されます。「このチャットを許可する」をクリックしてください。
Available Tools
1 toolperplexity_search_webC
Search the web using Perplexity AI with recency filtering
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| recency | No | month |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description bears full responsibility for behavioral disclosure. It does not mention whether the operation is read-only, any rate limits, authentication requirements, or side effects. The tool is likely a safe read operation but this is not confirmed, leaving the agent without critical behavioral context.
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 a single, concise sentence that efficiently conveys the core purpose. No redundant information is present. However, it could be slightly expanded to include more context without losing conciseness, hence a 4 rather than 5.
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?
For a tool with no output schema, no annotations, and two parameters with no schema descriptions, the description is severely lacking. It does not specify what the search returns (type of content, structure), any usage limits, or prerequisites. An agent cannot reliably determine if this tool is appropriate for a given task without additional external knowledge.
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?
Schema description coverage is 0%, meaning the schema provides no parameter descriptions. The description adds only 'with recency filtering' which hints at the recency parameter but does not explain the query parameter or the meaning of recency enum values (day, week, month, year). This is insufficient for an agent to form correct parameter values without additional knowledge.
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 resource ('web'), the specific service ('Perplexity AI'), and includes a key feature ('recency filtering'). This is specific and unambiguous, avoiding tautology by adding value beyond the tool name.
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?
No guidance is provided on when to use this tool or when to consider alternatives. Since there are no sibling tools, the lack of usage context is less critical, but the description could still mention typical use cases (e.g., 'for up-to-date web results with time constraints'). Currently, it merely rephrases the tool name.
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.
1 tool update
v1.0.0- First observed
perplexity_search_web
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool has a single, clear purpose that cannot be confused with any other tool in the set.
The single tool name follows a consistent verb_noun pattern (perplexity_search_web), and with only one tool, there is no inconsistency to evaluate. The naming is clear and descriptive.
One tool is too few for a server with a broad purpose like web search, as it lacks related operations such as filtering results, getting details, or handling different search types. This minimal set may limit agent functionality in practical scenarios.
The server is severely incomplete for web search functionality, offering only a basic search tool without capabilities like refining queries, paginating results, or accessing cached or specific types of content. This creates significant gaps for agents trying to perform comprehensive web searches.
Maintenance
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.
Web search, agentic search, news, page retrieval, sitemaps, and trending topics through Search1API.
MCP server for Google search results via SERP API
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