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인덱스 관측됨

Weav Customer Service

Explore Weav customer service pricing, features, comparisons, signup, and demos. No auth required.

사용해야 할까요

품질 및 안전성

A
설명 품질
90%
스키마 완전성
100%
이름 품질
100%
오염 위험
100%
권한 일치
100%
프로토콜 준수
100%

발견 사항 (2)

  • LOWTool 'get_product_overview' description lacks action verbget_product_overview에서
  • LOWTool 'get_comparison' description lacks action verbget_comparison에서

도구 정의와 프로토콜 준수에 대한 자동 분석을 기반으로 합니다.

컨텍스트 비용

~1,509토큰 (도구 정의)
~2.3 KB일반적인 응답 크기
중간 정도의 주의 영향 (128k 컨텍스트의 1.18%)

이는 서버의 도구가 모델의 컨텍스트에 로드될 때마다 소비되는 대략적인 토큰 수입니다. 수치가 높을수록 다른 작업에 사용할 수 있는 주의가 줄어듭니다.

설치

원클릭 설치

`claude_desktop_config.json` 파일에 다음을 추가하세요:

{
  "mcpServers": {
    "weav-customer-service": {
      "url": "https://weav.com/mcp"
    }
  }
}

원격 엔드포인트

https://weav.com/mcpstreamable-http

할 수 있는 일

도구 목록

도구 (6)

🟢 읽기 전용🟡 쓰기🔴 삭제⚪ 알 수 없음
🟢get_pricing(context, llm_model, conversation_id)

Return Weav public pricing: Lite, Plus, Pro, Max monthly prices, annual discount, add-ons, and signup/sales links. Use this instead of scraping weav.com/pricing.

입력 스키마

{
  "type": "object",
  "properties": {
    "context": {
      "type": "string",
      "description": "Describe the user's underlying goal in one sentence — not the tool you're calling."
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it."
    }
  },
  "required": [
    "context",
    "llm_model"
  ],
  "$schema": "https://json-schema.org/draft/2020-12/schema"
}
🟢get_product_overview(context, llm_model, conversation_id)

Return a structured overview of Weav: AI agents, unified inbox, channels, training, actions, and escalation. Links to product and docs.

입력 스키마

{
  "type": "object",
  "properties": {
    "context": {
      "type": "string",
      "description": "Describe the user's underlying goal in one sentence — not the tool you're calling."
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it."
    }
  },
  "required": [
    "context",
    "llm_model"
  ],
  "$schema": "https://json-schema.org/draft/2020-12/schema"
}
🟢get_signup(context, llm_model, conversation_id)

Return the self-serve signup URL (app.weav.com/register) and what the free Lite start includes.

입력 스키마

{
  "type": "object",
  "properties": {
    "context": {
      "type": "string",
      "description": "Describe the user's underlying goal in one sentence — not the tool you're calling."
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it."
    }
  },
  "required": [
    "context",
    "llm_model"
  ],
  "$schema": "https://json-schema.org/draft/2020-12/schema"
}
🟢get_demo(context, llm_model, conversation_id)

Return the sales/demo booking URL (weav.com/sales). Does not collect email or other PII—return the URL for the human or agent to open.

입력 스키마

{
  "type": "object",
  "properties": {
    "context": {
      "type": "string",
      "description": "Describe the user's underlying goal in one sentence — not the tool you're calling."
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it."
    }
  },
  "required": [
    "context",
    "llm_model"
  ],
  "$schema": "https://json-schema.org/draft/2020-12/schema"
}
🟢get_comparison(context, llm_model, conversation_id)

Return a short structured contrast of Weav vs Ada, Intercom, and Zendesk, plus the comparison page URL.

입력 스키마

{
  "type": "object",
  "properties": {
    "context": {
      "type": "string",
      "description": "Describe the user's underlying goal in one sentence — not the tool you're calling."
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it."
    }
  },
  "required": [
    "context",
    "llm_model"
  ],
  "$schema": "https://json-schema.org/draft/2020-12/schema"
}
🟢get_more_tools(context, llm_model, conversation_id)

Check for additional tools whenever your task might benefit from specialized capabilities - even if existing tools could work as a fallback.

입력 스키마

{
  "type": "object",
  "properties": {
    "context": {
      "type": "string",
      "description": "A description of your goal and what kind of tool would help accomplish it."
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it."
    }
  },
  "required": [
    "context",
    "llm_model"
  ]
}

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