WhatsApp 자동화를 위한 Evolution API 와 통합되는 Claude용 MCP(Model Context Protocol) 서버입니다.
이 MCP 서버를 통해 Claude는 Evolution API를 통해 WhatsApp과 상호 작용하여 다음과 같은 기능을 사용할 수 있습니다.
WhatsApp 인스턴스 관리
다양한 유형의 메시지 보내기
연락처 및 그룹 작업
웹후크 및 설정 구성
Related MCP server: Evolution API MCP Server
지엑스피1
Evolution API 자격 증명으로 .env 파일을 만듭니다.
EVOLUTION_API_URL=https://your-evolution-api-server.com
EVOLUTION_API_KEY=your-api-key-here환경 | 단계 | 명령 |
지역 개발 | 1. 복제 및 설치 2. 개발자 모드에서 실행 |
|
현지 생산 | 1. 복제 및 설치 2. 빌드 및 실행 |
|
도커 컴포즈 | Docker Compose로 실행 |
|
도커 | 컨테이너 빌드 및 실행 |
|
Claude Desktop 구성 파일(일반적으로 ~/Library/Application Support/Claude/claude_desktop_config.json 에 위치)에 다음을 추가합니다.
{
"mcpServers": {
"evo-api": {
"command": "node",
"args": [
"/path/to/your/mcp-evo-api/dist/main.js"
]
}
}
}Docker 배포를 사용하는 경우:
{
"mcpServers": {
"evo-api": {
"url": "http://localhost:3000"
}
}
}범주 | 구현됨 | 보류 중인 구현 |
핵심 API | ✅ 정보 가져오기✅ 인스턴스 생성✅ 인스턴스 가져오기✅ 인스턴스 연결✅ 인스턴스 다시 시작✅ 연결 상태 인스턴스 로그아웃✅ 인스턴스 삭제✅ 상태 설정 | ❌ 체크는 왓츠앱입니다 |
웹훅 및 설정 | ✅ 웹훅 설정✅ 웹훅 찾기✅ 설정 설정✅ 설정 찾기 | |
메시징 | ✅ 일반 텍스트 보내기 ✅ 상태 보내기 ✅ 미디어 보내기 ✅ WhatsApp 오디오 보내기 ✅ 스티커 보내기 ✅ 위치 보내기 ✅ 연락처 보내기 ✅ 반응 보내기 ✅ 여론조사 보내기 ✅ 목록 보내기 ✅ 버튼 보내기 | ❌ 메시지를 읽음으로 표시❌ 메시지를 읽지 않음으로 표시❌ 채팅 보관❌ 모든 사람에게서 메시지 삭제❌ 메시지 업데이트❌ 상태 메시지 보내기(채팅 Ctrl) |
채팅 및 연락처 | ✅ 연락처 찾기✅ 채팅 찾기 | ❌ 차단 상태 업데이트❌ 프로필 사진 URL 가져오기❌ Base64 가져오기❌ 메시지 찾기❌ 상태 메시지 찾기 |
여러 떼 | ✅ JID로 그룹 찾기✅ 모든 그룹 가져오기✅ 그룹 구성원 찾기 | ❌ 그룹 만들기❌ 그룹 사진 업데이트❌ 그룹 제목 업데이트❌ 그룹 설명 업데이트❌ 초대 코드 가져오기❌ 초대 코드 취소❌ 그룹 초대 보내기❌ 초대 코드로 그룹 찾기❌ 그룹 구성원 업데이트❌ 그룹 설정 업데이트❌ 임시 그룹 전환❌ 그룹 나가기 |
프로필 설정 | ❌ 비즈니스 프로필 가져오기❌ 프로필 가져오기❌ 프로필 이름 업데이트❌ 프로필 상태 업데이트❌ 프로필 사진 업데이트❌ 프로필 사진 제거❌ 개인정보 보호 설정 가져오기❌ 개인정보 보호 설정 업데이트 | |
봇 통합 | ❌ Typebot 통합❌ OpenAI 통합❌ Evolution Bot❌ Dify Bot❌ Flowise Bot | |
기타 통합 | ❌ Chatwoot❌ 웹소켓❌ SQS❌ RabbitMQ |
자세한 내용은 Evolution API 문서를 참조하세요.
Hello tool
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | The name of the person to greet |
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. 'Hello tool' reveals nothing about whether this is a read/write operation, what permissions might be required, what side effects occur, or what the response format looks like. The description fails to provide any behavioral context beyond the minimal implication from the name.
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?
While technically concise with only two words, this represents under-specification rather than effective conciseness. The description doesn't contain enough information to be useful, and the single phrase doesn't earn its place by providing meaningful guidance to an AI agent.
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 that there are no annotations and no output schema, the description should provide more complete context about what this tool does and what to expect. A single-parameter tool with 100% schema coverage could get by with minimal description, but 'Hello tool' fails to explain the basic purpose and behavior adequately for an AI agent.
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 fully documents the single 'name' parameter. The description adds no additional parameter information beyond what's in the schema. According to scoring rules, when schema 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 'Hello tool' is essentially a tautology that restates the tool name without specifying what it does. It doesn't provide a clear verb+resource combination or explain the actual function. While the name suggests greeting functionality, the description fails to articulate this explicitly.
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 absolutely no guidance about when to use this tool, what context it's appropriate for, or any prerequisites. There are no sibling tools mentioned, but even basic usage context is completely missing from the description text.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
hello_toolScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to compare it against. The single tool's purpose is inherently distinct by default.
A single tool cannot demonstrate inconsistency, as there are no other tool names to compare it to. The naming pattern for 'hello_tool' (snake_case) is consistent within the set, albeit trivially so.
A single tool is generally too few for most server purposes, as it limits functionality and suggests an incomplete or trivial implementation. While it might be appropriate for a minimal 'hello world' server, it is inadequate for any substantive domain coverage.
With only one tool named 'hello_tool', it is impossible to infer a meaningful domain or assess coverage. There are obvious gaps, as no CRUD operations, lifecycle management, or typical API interactions are present, making the surface severely incomplete for any practical purpose.
WhatsMCP connects Claude and other MCP-compatible AI agents directly to WhatsApp. Send and receive text, images, documents, and voice notes; manage groups (create, add/remove members, promote admins); look up contacts and profiles; follow channels; and read call and message history — all through a standard MCP interface. For voice use cases, WhatsMCP offers SIP-based calling plans (inbound-only, or full inbound/outbound) so AI voice agents can answer and place WhatsApp calls, plus low-latency WebSocket integrations with voice agent providers like ElevenLabs. Multiple WhatsApp accounts can be paired and managed per workspace, with webhook support for real-time inbound message delivery to your own infrastructure.
Atendio is a WhatsApp AI assistant for businesses in Latin America, on the official WhatsApp Business Platform. This MCP server lets Claude (or any MCP client) list and read WhatsApp conversations, view analytics and assistant settings, and create, edit or delete the rules your AI assistant follows. Remote, OAuth 2.1; the business always reviews and publishes rule changes.
Let Claude or ChatGPT search, read and send your WhatsApp messages over MCP. OAuth sign-in.
Hosted MCP server for your own WhatsApp accounts: messages, contacts, groups, channels, calls.
