Perplexity MCP Server
Perplexity MCP 서버
Perplexity의 API를 사용하여 웹 검색 기능을 제공하는 MCP 서버입니다.
필수 조건
Node.js(v14 이상)
Perplexity API 키( https://www.perplexity.ai/settings/api 에서 받으세요)
클로드 데스크톱 앱
Related MCP server: Perplexity Web Search MCP Server
설치
Smithery를 통해 설치
Smithery 를 통해 Claude Desktop용 Perplexity Server를 자동으로 설치하려면:
지엑스피1
수동 설치
이 저장소를 복제하세요:
git clone https://github.com/jaacob/perplexity-mcp cd perplexity-mcp종속성 설치:
npm install서버를 빌드하세요:
npm run build
구성
https://www.perplexity.ai/settings/api 에서 Perplexity API 키를 받으세요.
~/Library/Application Support/Claude/claude_desktop_config.json에 있는 Claude의 구성 파일에 서버를 추가합니다.
{
"mcpServers": {
"perplexity-server": {
"command": "node",
"args": [
"/absolute/path/to/perplexity-mcp/build/index.js"
],
"env": {
"PERPLEXITY_API_KEY": "your-api-key-here",
"PERPLEXITY_MODEL": "sonar"
}
}
}
}/absolute/path/to 저장소를 복제한 실제 경로로 바꾸세요.
사용 가능한 모델
PERPLEXITY_MODEL 환경 변수를 설정하여 사용할 모델을 지정할 수 있습니다. 사용 가능한 옵션은 다음과 같습니다.
sonar-reasoning-pro- 향상된 추론 기능을 갖춘 가장 유능한 모델sonar-reasoning- 향상된 추론 기능sonar-pro- 더 빠른 응답 시간sonar- 기본 모델(모델이 지정되지 않은 경우 사용됨)
최신 모델 가격 및 가용성을 확인하려면 https://docs.perplexity.ai/guides/pricing을 방문하세요.
용법
서버를 구성하고 Claude를 다시 시작한 후에는 Claude에게 정보 검색을 요청하기만 하면 됩니다. 예:
"SpaceX에 대한 최신 소식은 뭐예요?"
"시카고 최고의 레스토랑을 찾아보세요"
"재즈 음악의 역사에 대한 정보를 찾아보세요"
클로드는 자동으로 Perplexity 검색 도구를 사용하여 관련 정보를 찾아 반환합니다.
어떤 이유로든 검색이 실행되지 않으면 프롬프트 앞에 "웹 검색"을 추가하여 강제로 문제를 해결할 수 있습니다.
개발
서버를 수정하려면:
src/index.ts편집하세요npm run build로 다시 빌드하세요변경 사항을 로드하려면 Claude를 다시 시작하세요.
특허
MIT
Available Tools
1 toolsearchC
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 specify rate limits, authentication requirements, result format, pagination, or any other behavioral characteristics. The description is minimal and lacks important operational 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 extremely concise - a single sentence that directly states the tool's function. There's zero waste or unnecessary verbiage. It's appropriately sized for a simple search tool 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?
For a search tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what kind of results to expect, whether there are limitations on query types, how results are formatted, or any other operational details. The description leaves too many questions unanswered for effective tool usage.
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 'query' parameter clearly documented. The description doesn't add any additional parameter semantics beyond what's already in the schema. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no parameter information in the description.
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 specifies the resource/context ('using Perplexity AI'), making the purpose immediately understandable. It's not a tautology and provides specific information about what the tool does. However, since there are no sibling tools mentioned, it cannot demonstrate differentiation from alternatives.
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 specific contexts where it's most appropriate. It simply states what the tool does without any usage context 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
v1.0.0- First observed
search
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools, making disambiguation perfect. The tool's purpose is clearly defined as web search using Perplexity AI, leaving no room for misselection.
A single tool named 'search' follows a consistent and straightforward verb-based naming pattern. There are no other tools to compare against, so no inconsistency can exist in the naming scheme.
One tool is too few for a server named 'Perplexity MCP Server', which implies broader capabilities beyond just search. A single tool feels thin and under-scoped for what might be expected from a Perplexity AI integration, such as additional operations like summarization or follow-up queries.
The tool surface is severely incomplete for a web search domain; it only offers a basic search function without any complementary tools for refining results, getting summaries, or handling related queries. This creates significant gaps that could lead to agent failures in complex tasks.
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.
Provides AI assistants with access to Seltz's powerful Web Search capabilities.
Web search, agentic search, news, page retrieval, sitemaps, and trending topics through Search1API.
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
- FlicenseNot gradedqualityDmaintenanceAllows Claude or other MCP-compatible AI assistants to search the web and get up-to-date information using the Perplexity API, with features for filtering results by time period.8-
- AlicenseNot gradedqualityDmaintenanceIntegrates the Sonar API to provide Claude with real-time web-wide research capabilities. Enables conversational web searches through Perplexity's AI-powered search engine for up-to-date information retrieval.1,406 npmMIT
- AlicenseNot gradedqualityDmaintenanceExposes Perplexity AI's search capabilities to Claude, enabling real-time web search and information retrieval within the assistant. The project is currently in active development with plans to support Perplexity Spaces and multi-source data synthesis.Apache 2.0