이 저장소에는 Perplexity AI API에 대한 액세스를 제공하는 모델 컨텍스트 프로토콜(MCP) 서버의 소스 코드가 포함되어 있습니다. 이 서버를 통해 사용자는 채팅, 검색, 문서 조회 등 다양한 도구를 통해 Perplexity AI와 상호 작용할 수 있습니다.
이 서버는 Perplexity AI를 MCP 기반 시스템에 통합하는 과정을 간소화합니다. Perplexity AI의 기능에 편리하고 표준화된 방식으로 접근할 수 있도록 지원합니다.
Related MCP server: perplexity-mcp-server
Node.js와 npm 설치: 시스템에 Node.js와 npm이 설치되어 있는지 확인하세요.
저장소 복제: 이 저장소를 로컬 컴퓨터에 복제합니다.
종속성 설치: 프로젝트 디렉토리로 이동하여 npm install 실행합니다.
API 키 구성: PERPLEXITY_API_KEY 환경 변수를 Perplexity API 키로 설정합니다.
서버 실행: npm start 실행하여 서버를 시작합니다.
이 서버는 MCP 시스템을 통해 접근할 수 있는 여러 도구를 제공합니다. 이러한 도구의 사용 방법에 대한 자세한 내용은 MCP 설명서를 참조하십시오.
타입스크립트
@modelcontextprotocol/sdk
악시오스
Perplexity API는 신뢰할 수 없습니다. API 오류를 원활하게 처리하기 위해 오류 처리 기능이 포함되어 있습니다.
기여를 환영합니다! 이슈를 개설하거나 풀 리퀘스트를 제출해 주세요.
Maintains ongoing conversations with Perplexity AI. Creates new chats or continues existing ones with full history context.
| Name | Required | Description | Default |
|---|---|---|---|
| message | Yes | The message to send to Perplexity AI | |
| chat_id | No | Optional: ID of an existing chat to continue. If not provided, a new chat will be created. |
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 'maintains ongoing conversations' and 'full history context', which implies statefulness and persistence, but doesn't disclose critical behavioral traits such as authentication requirements, rate limits, conversation length limits, whether it's read-only or mutative, error handling, or what happens when chat_id is invalid. The description adds some context but leaves significant gaps for a tool that likely involves API calls and state management.
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 appropriately sized with two concise sentences that are front-loaded with the main purpose. Every sentence earns its place: the first establishes the core functionality, and the second clarifies the chat creation/continuation behavior. There's no wasted verbiage or redundant 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 tool's complexity (managing conversational state with an external AI service), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., AI response format, chat_id for new chats), error conditions, authentication needs, or operational constraints. For a stateful chat tool with external dependencies, this minimal description leaves too many unknowns for effective agent use.
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 description doesn't explicitly mention or explain any parameters. However, with 100% schema description coverage, the input schema already provides clear documentation for both parameters (message and chat_id). The description's mention of 'creates new chats or continues existing ones' aligns with the chat_id parameter's semantics, but adds no additional meaning beyond what the schema already states. This meets the baseline of 3 when schema coverage is high.
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 tool's purpose with specific verbs ('maintains', 'creates', 'continues') and resources ('ongoing conversations with Perplexity AI', 'new chats', 'existing ones'). It distinguishes from siblings by focusing on conversational AI interactions rather than code analysis, API discovery, documentation retrieval, or general search. However, it doesn't explicitly differentiate from potential sibling tools that might also involve chat interactions.
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 implies usage context for maintaining conversations with Perplexity AI, but provides no explicit guidance on when to use this tool versus the sibling tools (check_deprecated_code, find_apis, get_documentation, search). It mentions the ability to create new chats or continue existing ones, which gives some implied usage scenarios, but lacks clear when/when-not instructions or alternative recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check if code or dependencies might be using deprecated features
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | The code snippet or dependency to check | |
| technology | No | The technology or framework context (e.g., 'React', 'Node.js') |
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 checks for deprecated features but doesn't describe how it performs this check, what the output looks like, any limitations (e.g., accuracy, supported technologies beyond examples), or potential impacts (e.g., if it modifies code). This leaves significant gaps in understanding the tool's behavior.
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 directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, though it could be slightly more structured (e.g., by including usage hints) to earn a perfect score.
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 checking deprecated code (which involves analysis and potential output interpretation), the description is incomplete. No annotations exist to provide behavioral context, and there's no output schema to explain return values. The description alone lacks details on how results are presented, accuracy, or scope, making it inadequate for full understanding.
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, clearly documenting both parameters ('code' and 'technology') with examples. The description adds no additional meaning beyond this, such as explaining parameter interactions or constraints. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
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 tool's purpose with a specific verb ('Check') and resource ('code or dependencies'), and identifies what it checks for ('deprecated features'). However, it doesn't explicitly differentiate from sibling tools like 'find_apis' or 'get_documentation', which might also relate to code analysis, 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. It doesn't mention any specific contexts, prerequisites, or exclusions, nor does it reference sibling tools like 'search' or 'find_apis' for related tasks, leaving usage entirely implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find and evaluate APIs that could be integrated into a project
| Name | Required | Description | Default |
|---|---|---|---|
| requirement | Yes | The functionality or requirement you're looking to fulfill | |
| context | No | Additional context about the project or specific needs |
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 finds and evaluates APIs but doesn't explain how it does this (e.g., search methods, sources, criteria), what the output looks like, or any limitations like rate limits or authentication needs. This leaves significant gaps for an agent to understand its behavior.
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 directly states the tool's purpose without unnecessary words. It is front-loaded with the core function, though it could be slightly more structured by separating finding from evaluating aspects.
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 tool's complexity (finding and evaluating APIs), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral traits, output format, or integration specifics, leaving the agent with insufficient context to use the tool effectively beyond basic parameter input.
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 parameters 'requirement' and 'context' clearly documented. The description adds no additional meaning beyond what the schema provides, such as examples or usage context for the parameters. Since schema coverage is high, the baseline score of 3 is appropriate.
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 tool's purpose as 'Find and evaluate APIs that could be integrated into a project', which includes a specific verb ('find and evaluate') and resource ('APIs'). It distinguishes itself from siblings like 'search' by specifying API evaluation for integration, though it doesn't explicitly contrast with other tools like 'get_documentation'.
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. It doesn't mention when to choose it over sibling tools like 'search' for general queries or 'get_documentation' for API details, nor does it specify prerequisites or exclusions for its use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get documentation and usage examples for a specific technology, library, or API
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The technology, library, or API to get documentation for | |
| context | No | Additional context or specific aspects to focus on |
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 retrieves documentation and examples but doesn't describe how it works (e.g., sources, format, limitations, rate limits, or error handling). For a tool with no annotations, this leaves significant gaps in understanding its behavior.
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 with zero waste. It's front-loaded with the core purpose and appropriately sized for the tool's complexity.
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 tool's moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It states the purpose but lacks details on behavior, usage context, and output format, which are needed for full understanding without annotations or output schema.
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 100%, so the schema already documents both parameters ('query' and 'context') with clear descriptions. The description adds no additional meaning beyond what the schema provides, such as examples or constraints, but doesn't need to compensate for low coverage.
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 tool's purpose: 'Get documentation and usage examples for a specific technology, library, or API.' It specifies the verb ('Get') and resource ('documentation and usage examples'), but doesn't explicitly differentiate from sibling tools like 'find_apis' or 'search', which might have overlapping functionality.
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. It doesn't mention sibling tools like 'find_apis' or 'search', nor does it specify prerequisites, exclusions, or contextual triggers for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Perform a general search query to get comprehensive information on any topic
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The search query or question | |
| detail_level | No | Optional: Desired level of detail (brief, normal, detailed) |
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 mentions 'comprehensive information' but does not specify what that entails (e.g., format, sources, pagination, rate limits, or permissions). This is a significant gap for a search tool with no structured safety or operational hints.
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 is front-loaded with the core purpose. However, it could be more structured by explicitly mentioning the parameters or usage context, but it avoids unnecessary verbosity.
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 no annotations and no output schema, the description is incomplete. It does not explain what the tool returns (e.g., results format, limitations) or behavioral traits like error handling. This leaves critical gaps for an agent to use the tool effectively.
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 100%, so the schema already documents both parameters ('query' and 'detail_level') with descriptions and an enum. The description adds no additional meaning beyond what the schema provides, such as examples or context for the 'detail_level' options. Baseline 3 is appropriate when the schema does the heavy lifting.
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 states the tool 'perform[s] a general search query to get comprehensive information on any topic', which provides a clear verb ('perform') and resource ('search query'), but it is vague about what type of information or sources are searched. It does not distinguish from siblings like 'find_apis' or 'get_documentation', leaving ambiguity about scope.
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 offers no guidance on when to use this tool versus alternatives such as 'chat_perplexity' or 'find_apis'. It implies a broad context ('any topic') but lacks explicit when/when-not instructions or prerequisites, leaving the agent to guess based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Most tools have distinct purposes: chat_perplexity handles conversational AI, check_deprecated_code analyzes code deprecation, find_apis discovers APIs, get_documentation retrieves docs, and search performs general queries. However, get_documentation and search could overlap slightly when users seek technical information, but descriptions clarify their focus.
Naming is mixed: chat_perplexity uses a verb_noun format, while check_deprecated_code, find_apis, get_documentation, and search use verb-based phrases without a consistent pattern. All names are snake_case, providing some readability, but the verb styles vary (e.g., 'chat' vs. 'check' vs. 'find'), lacking a unified convention.
With 5 tools, the count is reasonable for a server focused on AI assistance and information retrieval. It covers key areas like conversation, code analysis, API discovery, documentation, and general search, though it might feel slightly thin for broader AI tasks, but each tool earns its place.
The server targets AI-driven information and code assistance, with tools for chat, code checks, API finding, documentation, and search. Notable gaps include lack of update/delete operations for chats or saved searches, and no tool for summarizing or analyzing search results, which could limit agent workflows in this domain.
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