Perplexity AI MCP Server
Perplexity AI MCP サーバー
Perplexity AI の API を統合し、複数のモデル オプションによる高度な検索機能を提供する MCP サーバー実装。
特徴
Perplexity AIのモデルを使った検索
すべての公式Sonarモデルをサポート
設定可能な結果数
詳細なエラー処理とログ記録
MCP Inspector対応
Related MCP server: PPX-MCP
利用可能なモデル
sonar-reasoning-pro (127k コンテキスト)
ソナー推論 (127k コンテキスト)
sonar-pro (200kコンテキスト)
ソナー(127kコンテキスト)
インストール
pnpm install
pnpm build構成
APIキー
Perplexity AIアカウントに登録する
ダッシュボードからAPIキーを取得する
環境変数を設定します。
export PERPLEXITY_API_KEY=your_api_key_here使用法
直接実行
node dist/index.js
# or if you made it executable
./dist/index.js発達
pnpm devMCP Inspectorによるテスト
pnpm inspectツールリファレンス
困惑_検索
Perplexity AI のモデルを使用して検索を実行します。
パラメータ:
query(文字列、必須): 検索クエリmodel(文字列、オプション): 使用するモデル (デフォルト: "sonar")利用可能なオプション: sonar-reasoning-pro、sonar-reasoning、sonar-pro、sonar
count(数値、オプション): 結果の数 (1-10、デフォルト: 5)
発達
プロジェクト構造
src/perplexity/
├── index.ts # Main server implementation
├── lib/
│ └── logger.ts # Logging configuration
└── adr.md # Architectural decisionsログ記録
ログは、プロジェクト ルート ディレクトリのperplexity-mcp.logに書き込まれます。
ライセンス
マサチューセッツ工科大学
貢献
リポジトリをフォークする
機能ブランチを作成する
変更をコミットする
ブランチにプッシュする
新しいプルリクエストを作成する
Available Tools
1 toolperplexity_searchC
Search using Perplexity AI's models with context-aware responses and citations
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| model | No | Model to use (sonar-reasoning-pro, sonar-reasoning, sonar-pro, sonar) | sonar |
| count | No |
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. It mentions 'context-aware responses and citations', which adds some value about output characteristics, but fails to address critical aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or error handling. For a search tool with zero annotation coverage, this leaves significant gaps.
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, efficient sentence that conveys the core functionality without unnecessary words. It's appropriately sized and front-loaded with the essential information.
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 the complexity of a search tool with 3 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns (beyond mentioning 'responses and citations'), doesn't cover parameter meanings beyond what little the schema provides, and leaves behavioral aspects unclear. This is inadequate for proper tool selection and invocation.
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 only 33% (only the 'model' parameter has a description), so the description needs to compensate but doesn't mention any parameters. The baseline would be lower, but since there are only 3 parameters and one is well-documented in the schema, the description's failure to add parameter context results in a minimal viable score.
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') and the resource ('Perplexity AI's models'), specifying it provides 'context-aware responses and citations'. However, with no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, so it doesn't reach the highest score.
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 alternatives, prerequisites, or exclusions. It only states what the tool does without contextual usage information.
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
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as searching using Perplexity AI's models, making it distinct by default.
The single tool name 'perplexity_search' follows a clear and consistent verb_noun pattern (search as the verb, perplexity as a modifier). With only one tool, naming consistency is inherently perfect as there are no other tools to compare against.
A single tool is generally too few for most server purposes, as it limits functionality and flexibility. For a search-focused server, additional tools like filtering, citation management, or model selection might be expected to provide a more complete experience.
The server's domain appears to be search using Perplexity AI, but with only one search tool, there are significant gaps. Missing operations could include advanced search parameters, result refinement, citation handling, or integration with other Perplexity features, making the surface incomplete for typical search workflows.
Maintenance
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Related MCP Servers
- MIT
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- AlicenseNot gradedqualityFmaintenanceAn MCP server that integrates with Poe's API to provide AI-powered search and research tools from Perplexity, Reka, Exa, and Linkup.19 npm8MIT
- AlicenseNot gradedqualityDmaintenanceAn MCP server that enables AI agents to perform search-augmented queries and deep multi-source research using the Perplexity API.59 npm25Apache 2.0