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Perplexity Tool for Claude Desktop

by letsbuildagent
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Claude 桌面版的困惑工具

一个自定义的 MCP 工具，将 Perplexity AI 的 API 与 Claude Desktop 集成，允许 Claude 执行基于网络的研究并提供带有引文的答案。

先决条件安装

  1. 安装 Git：

    • 对于Mac：

      • 首先通过在终端中粘贴以下内容来安装Homebrew ：GXP1

      • 然后安装Git：GXP2

    • 对于 Windows：

  2. 安装 Node.js：

    • 对于Mac：

      brew install node

    • 对于 Windows：

  3. 通过运行以下命令验证安装：

git --version
node --version

Related MCP server: Perplexity MCP Server

工具安装

  1. 克隆存储库

git clone https://github.com/letsbuildagent/perplexity-tool
cd perplexity-tool

  1. 安装依赖项

npm install

  1. 设置您的 API 密钥

您有两个选择：

选项 1（快速设置）：

  • 打开server.js

  • 找到这一行：

const PERPLEXITY_API_KEY = "YOUR-API-KEY-HERE";

  • 用您的 Perplexity API 密钥替换

选项 2（最佳实践）：

  • 创建 .env 文件：

    # On Mac/Linux:
touch .env
open .env

# On Windows:
notepad .env

    或者简单地在文本编辑器中创建一个名为.env的新文件

  • 将您的 API 密钥添加到 .env 文件：

    PERPLEXITY_API_KEY=your-api-key-here

  • 安装 dotenv：

    npm install dotenv

  • 更新server.js：

    import 'dotenv/config'
const PERPLEXITY_API_KEY = process.env.PERPLEXITY_API_KEY;

  1. 配置 Claude 桌面

  • 打开~/Library/Application Support/Claude/claude_desktop_config.json

  • 添加此配置：

{
  "mcpServers": {
    "perplexity-tool": {
      "command": "node",
      "args": [
        "/full/path/to/perplexity-tool/server.js"
      ]
    }
  }
}

将/full/path/to替换为您克隆存储库的实际路径。

  1. 重启Claude桌面

用法

安装完成后，您可以通过 Claude 使用以下命令使用该工具：

  • “向 Perplexity 询问人工智能的最新发展”

  • “使用 Perplexity 研究量子计算的历史”

  • “在 Perplexity 上搜索有关气候变化的信息，重点关注上个月”

高级选项

您可以指定其他参数：

  • temperature ：控制响应随机性（0-2）

  • max_tokens ：限制响应长度

  • search_domain_filter ：将搜索限制在特定域

  • search_recency_filter ：按时间段（日/周/月/年）过滤

故障排除

  1. 未找到 Git：

    • 确保你已正确安装 Git

    • 尝试重启你的终端

    • 在 Mac 上，确保 Homebrew 位于你的 PATH 中

  2. Node.js 错误：

    • 使用node --version验证 Node.js 安装

    • 尝试重新安装 Node.js

  3. API 密钥问题：

    • 确保您已正确复制 API 密钥

    • 检查 .env 文件中没有多余的空格

    • 如果使用选项 2，请验证是否已安装 dotenv

  4. 工具未出现在 Claude 中：

    • 检查claude_desktop_config.json中的路径

    • 确保路径指向你的 server.js 文件

    • 重启Claude桌面

    • 检查控制台是否有任何错误消息

执照

麻省理工学院

安全说明

如果您打算分享您的代码或将其公开：

  • 不要将您的 API 密钥提交给 Git

  • 使用 .env 方法（选项 2）

  • 将 .env 添加到你的 .gitignore 文件

Available Tools

1 tool
ask_perplexityC

Ask a question to Perplexity AI

ParametersJSON Schema
NameRequiredDescriptionDefault
questionYesThe question to ask
temperatureNoResponse randomness (0-2)
max_tokensNoMaximum tokens in response
search_domain_filterNoLimit search to specific domains
search_recency_filterNoFilter results by recencymonth

TDQS

C2.7/5.0
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 states the tool 'Ask a question to Perplexity AI,' which implies it's a query tool, but doesn't describe what happens—e.g., whether it performs web searches, generates responses, has rate limits, or requires authentication. This is a significant gap for a tool with multiple parameters and no output schema.

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, clear sentence with no wasted words. It's front-loaded and efficiently conveys the core action, making it easy to scan and understand quickly.

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 (5 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the tool returns, how it behaves (e.g., search-based vs. generative), or any constraints. The agent must rely heavily on the schema and tool name, which is insufficient for effective use.

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, so parameters like 'question,' 'temperature,' and 'search_recency_filter' are well-documented in the schema. The description adds no additional meaning beyond the schema, such as explaining how parameters interact or typical use cases. This meets the baseline of 3 since the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Ask a question to Perplexity AI' clearly states the action (ask) and target (Perplexity AI), which is adequate. However, it's somewhat vague about what Perplexity AI is or does—it doesn't specify if this is for general queries, research, or something else. With no sibling tools, differentiation isn't needed, but the purpose could be more specific.

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—it doesn't mention use cases, prerequisites, or alternatives. With no sibling tools, there's no need to differentiate, but it lacks any context for appropriate usage, leaving the agent to infer based on the tool name alone.

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 updatev1.0.0
    • Addedask_perplexity
Install Server
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TDQS

C2.9/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'ask_perplexity' has a clearly distinct purpose that cannot be confused with any other tool in this server.

Naming Consistency5/5

The single tool name follows a clear verb_noun pattern ('ask_perplexity'), and with only one tool, there is perfect consistency. No other naming conventions exist to create inconsistency.

Tool Count2/5

A single tool is generally too few for most server purposes, as it provides minimal functionality and limits agent capabilities. For a Perplexity AI integration, one tool might suffice for basic queries, but it feels thin and lacks operations like follow-up questions or context management.

Completeness2/5

The tool surface is severely incomplete for interacting with Perplexity AI. While 'ask_perplexity' covers basic queries, there are significant gaps such as no support for conversation history, context setting, or handling different query types, which will likely cause agent failures in complex tasks.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

Resources

Unclaimed servers have limited discoverability.

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Related MCP Connectors

Related MCP Servers

  • Perplexity MCP Server

    RossH121
    A
    license
    A
    quality
    D
    maintenance
    An MCP server that enables Claude to perform web searches using Perplexity's API with intelligent model selection based on query intent and support for domain and recency filtering.
    6
    4
    MIT

  • Perplexity MCP Server

    Rohit-Seelam
    F
    license
    A
    quality
    D
    maintenance
    Integrates Perplexity AI's search-enhanced language models with Claude Desktop, providing three tools with different complexity levels for quick fact-checking, technical analysis, and deep research.
    3
    2
    -

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