A
licenseB
qualityF
maintenanceA server facilitating web search functionality by utilizing Perplexity AI's API, designed to integrate with the Claude desktop client for enhanced search queries.
1
308
MIT
Perplexity MCP Server
Perplexity MCP 服务器
这是一个简单的 MCP 服务器，允许您使用 Perplexity AI 搜索网络。
安装
先决条件
Node.js 18+（从nodejs.org下载）
Git（从git-scm.com下载）
Perplexity AI API 密钥
Windows 系统步骤
克隆存储库：
git clone [repository-url]
cd perplexity-mcp-server安装依赖项：
npm install创建环境文件：
在根目录中创建一个名为
.env的新文件添加您的 Perplexity AI API 密钥：GXP3
Related MCP server: Tavily MCP Server
在 Windows 上的使用
开发模式
npm run dev生产模式
构建项目：
npm run build启动服务器：
npm start使用 Windows 批处理文件（推荐）
为了避免 Windows 上的环境变量问题，请使用附带的批处理文件：
首先构建项目：
npm run build运行批处理文件：
.\run-perplexity-server.bat或者，双击 Windows 资源管理器中的run-perplexity-server.bat文件。
有关详细说明，请参阅Windows 安装指南。
解决 Windows 问题
常见问题
权限错误
以管理员身份运行命令提示符或 PowerShell
检查项目目录中的文件权限
环境变量
确保
.env文件位于根目录中.env文件中=符号周围没有空格修改环境变量后重启终端
使用提供的批处理文件来避免环境变量问题
Node.js 问题
验证 Node.js 安装：
node --version确保已安装 npm：
npm --version
错误消息
如果您看到Error: ENOENT: no such file or directory ，请确保：
所有路径均使用正确的 Windows 样式分隔符
您在正确的目录中
所需文件存在
项目结构
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 使用
该服务器提供了一个用于网络搜索的单一工具perplexity_search ：
{
"name": "perplexity_search",
"arguments": {
"query": "your search query here"
}
}执照
麻省理工学院
Available Tools
1 toolperplexity_searchC
Search the web using Perplexity AI
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The search query |
TDQS
C2.9/5.0
Behavior2/5
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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters3/5
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.
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/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.
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 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.
1 tool update
- First observed
perplexity_search
TDQS
B3.2/5.0
Scored across 1 tool
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, making it impossible for an agent to misselect between multiple options.
Naming Consistency5/5
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.
Tool Count2/5
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.
Completeness3/5
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
ActivityInactive
ResponsivenessNo issues
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.
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